Post on 12-Jan-2017
transcript
Discovering the Relationship between Macroeconomic Trends and Regional Theme Park
Performance
Dissertation Manuscript
Submitted to Northcentral University
Graduate Faculty of the School of Business
in Partial Fulfillment of the
Requirements for the Degree of
DOCTOR OF BUSINESS ADMINISTRATION
by
Christopher Peak
Prescott Valley, Arizona
November 2016
ii
Abstract
Consumer spending is a highly-researched topic and has been evaluated by multiple
industries. With consumer spending accounting for large amounts of gross domestic
product in the United States, understanding the impact of the consumer confidence index,
stock market values, interest rates, unemployment rates, and the consumer credit index on
spending trends is crucial to predicting reactions from changing macroeconomic
conditions. The specific problem is that consumer discretionary spending is impacted by
macroeconomic trends and although there is research on the impacts of macroeconomic
trends in relationship to various industries, there is no current information on how
changes in macroeconomic trends impact regional theme park attendance and overall
revenue results. The purpose of this quantitative ex post facto study was to understand
how macroeconomic indicators including consumer confidence index, stock market
values, interest rates, unemployment rates, and the consumer credit index impacted
regional theme park attendance and revenue during times of different macroeconomic
conditions. The research was conducted using annual data spanning 2007-2012,
specifically analyzing the correlation of these metrics to macroeconomic trends for each
year. Annual financial reports from both Six Flags and Cedar Fair Entertainment were
gathered for the needed attendance and revenue performance metrics as the dependent
variables for each year evaluated. Multiple regression analysis was used to conduct
statistical analysis on regional theme park attendance and total revenue performance
during times of macroeconomic shifts on an annual basis. While all independent variables
combined did not result in null hypothesis being rejected, specific independent variables
drove significant results that drove predictability within the models. Future research
iii
could include additional internal data from the regional theme park industry, along with
additional macroeconomic variables to predict future performance for the industry and
individual companies alike.
iv
Table of Contents
Chapter 1: Introduction 1
Background 3Statement of the Problem 6Purpose of the Study 7Research Questions 7Nature of the Study 10Significance of the Study 11Definition of Key Terms 13Summary 14
Chapter 2: Literature Review 17
Documentation 17Theory of Planned Behavior 18Consumer Spending 21Regional Theme Parks 26Consumer Confidence 33Stock Market Values 37Interest Rates 43Employment Rates 50Consumer Confidence Index 55Summary 57
Chapter 3: Research Method 58
Research Methods and Design(s) 61Population 62Sample 63Materials/Instruments 63Operational Definition of Variables (Quantitative/Mixed Studies Only)64Data Collection, Processing, and Analysis 66Assumptions 68Limitations 68Ethical Assurances 69Summary 69
Chapter 4: Findings 70
Results 71Evaluation of Findings 81Summary 83
Chapter 5: Implications, Recommendations, and Conclusions 84
Implications 84Recommendations87Conclusions 88
References 90
v
1
Chapter 1: Introduction
Consumer spending is a highly-researched topic and has been evaluated for a variety of
industries. Researchers have analyzed the relationship between macroeconomic conditions and
spending by consumers specifically in the retail industry (Abaidoo, 2014). A recent study
indicated that macroeconomic trends have an impact on the short run spending habits when
macroeconomic trends are negative (Abaidoo, 2014). This same study also concluded that
uncertainty and volatility in macroeconomic trends also created both short and long-term
spending declines (Abaidoo, 2014). In a similar manner, consumer spending patterns and fiscal
policy decisions were directly linked to macroeconomic variables (Ishmihan & Ozkan, 2011).
Since the Great Financial Crisis of 2008, the focus among scholars on consumer spending
has continued to expand. Consumer confidence trends based on lowered income expectations
have been connected to spending restraint by consumers in the short run (Gomes, 2010). This
same study concluded that the reaction to these short-term spending shifts should not cause an
overreaction by businesses that would drastically change long-term strategies used in planning
processes (Gomes, 2010).
Stock market rates and trends are frequently viewed macroeconomic values for many
consumers daily. Increases in consumer spending follow times of increased stock wealth in an
economy, along with expanded investment in the stock market itself (Hsu, Lin, & Wu, 2011a).
This two-way relationship between stock market values and consumer spending was verified by
multiple statistical testing methods in the study (Hsu et al., 2011a; Hsu, Lin, & Wu, 2011b).
Unemployment rates were found to impact consumer spending differently in a recent
study (Florea & Moise, 2014) . Increased employment did not directly lead to an increase in
spending. On the other hand, consumers did not spend less due to high unemployment. Instead,
2
they used savings and credit accounts to maintain their current spending habits (Florea & Moise,
2014).
Consumer credit indexes, especially among younger members of the economy, have been
a driving force of consumer spending in recent years (Schooley & Worden, 2010). The
increased comfort level among younger households regarding the use of debt to purchase
everyday goods amplifies the importance of credit availability for this subset of consumers.
When interest rates are expected to rise, these younger consumers react by borrowing to spend
immediately at lower levels of interest (Schooley & Worden, 2010).
With consumer spending accounting for more than half of the gross domestic product in
the United States, understanding the impact of the consumer confidence index, stock market
values, interest rates, unemployment rates, and the consumer credit index on spending trends is
crucial to predicting reactions from changing macroeconomic conditions. Of these
macroeconomic trends, unemployment rates, and consumer confidence have proven to be the
greatest drivers of ongoing consumer spending trends (Bryant & Macri, 2005). Understanding
the impact of these macroeconomic trends on consumer spending is vital for policymakers and
business leaders alike, especially in times of shifting macroeconomic conditions where swift
decisions need to be made.
The two-leading regional theme park organizations in the United States are Cedar Fair
Entertainment Company and Six Flags Entertainment Corporation. Each corporation has
multiple parks in various locations across the country. Their financial results depend on driving
revenue through attendance and guest spending over the course of each season. Understanding
the correlation between these macroeconomic indicators and their performance drivers is crucial
3
when predicting attendance and revenue performance, especially during times of changing
macroeconomic trends.
Background
In the public annual report for 2015, Six Flags described their company as the largest
regional theme park operator in the world. This report also described the sixteen U.S. theme
parks, servicing each of the top ten designated market areas as defined by A.C. Nielsen Media
Research. The current corporate structure was established in 1998 and now has over twenty-
eight million visitors a year. In addition to the revenue generated by admissions, in park
spending including food, merchandise, and extra fee activities makes up almost forty percent of
the company’s annual total revenue. The other leader in the United States regional theme park
industry, Cedar Fair Entertainment, also described their company activities in a similar public
annual report generated for 2015. With visitation totaling over twenty-four million guests with
theme parks in nine states across America and one location in Canada, Cedar Fair Entertainment
has also established itself as a leader in the regional theme park industry. Like their closest
competitor, Cedar Fair Entertainment generates over thirty-two percent of its total revenue
through in park spending that includes food, merchandise, and extra charge activities purchases.
With attendance at the parks and the ability of their guest to spend on items once they enter the
park being essential to the revenue performance for both companies, macroeconomic trends that
impact these spending drivers are of increasing importance to both companies and the industry as
a whole (Salamat & Banik, 2013b).
Since the Great Financial Crisis of 2008, consumer spending and the macroeconomic
factors impacting this spending has been a major focus for economists and businesses alike
(Gomes, 2010) . With consumer spending that climbed to over seventy percent of the gross
4
domestic product before the crisis, many businesses and the economy were overly dependent on
this trend that was primarily fueled by increased consumer debt (Yerex, 2011. ) . The increased
debt levels used by consumers was made available using low-cost loans, increases in revolving
credit card debt, and home equity loans backed by inflated home values (Yerex, 2011. ). The
spending bubble that would eventually burst was the result of consumer’s inherent drive to
consume at the highest level possible using this increased availability of income that was fueled
by debt (Yerex, 2011). In addition to the availability of debt, consumer’s confidence in their
future income levels has been cited a major factor for consumers when considering spending on
many levels (Eastman, McKay, & Forehand, 2010). Consumers’ perceptions of the economy,
personal financial stability, and cost of living expectations have been cited as factors impacting
planned spending expectations (Eastman, McKay, & Forehand, 2010). While these perceptions
cannot be directly related to one specific input, multiple macroeconomic factors have been cited
as leading indicators used to predict consumer confidence levels (Duarte Alonso et al., 2015). A
study examining the Christmas shopping season of 2008 described the impact of negative
consumer confidence levels on retail spending. This study saw a decline of a four percent in
sales losses versus just one year earlier, which was near an all-time high (Eastman, McKay, &
Forehand, 2010). Consumer spending has been similarly linked to interest rate levels,
specifically regarding long-term purchases (Barnes & Olivei, 2013). While lower interest rates
have shown a positive impact on current spending levels, future worries of inflation have led to
lower planned spending during these times with low- interest rates (Ichiue & Nishiguchi, 2015).
While it’s impact on consumer spending has not shown the same direct impact, stock
market values have directly impacted various factors that feed into consumer spending trends
(Sum, 2014). Younger consumers have been less risk adverse during times of stock market
5
declines with regards to short term purchasing decision, but the same has not held true for larger
purchase decisions with longer impacts on the consumer’s financial situation (Johnson & Naka,
2014). While this finding is positive for businesses selling consumable goods, the same cannot
be said for companies with larger priced items with a perceived long-term financial impact
(Johnson & Naka, 2014). In recent years with stock market growth, consumer spending trends
have followed with increased spending levels and expectations (DeLisle, 2013). With regards to
consumer spending levels during times of higher unemployment, varying results have been
generated by scholarly research. One such study concluded that higher unemployment levels led
to lower consumer spending in the months that would follow (Howard & Shipps, 2013). The
study also noted the trickle down impact that these decreased levels of spending would have on
landlords and banks holding loans for the laid off employees (Howard & Shipps, 2013). In
contradiction to this study, another researcher found that consumers have been shown to sustain
consistent levels of spending even during times of unemployment (Heim, 2010a). To continue
the same levels of spending, unemployed consumers used savings, took help from friends, or
increased levels of debt to maintain their current lifestyle (Heim, 2010a).
The consumer credit index, or the amount of available credit with the economy, is
another driver of consumer spending (Bearden & Haws, 2012). While some had considered
more available credit to be a positive impact on consumer spending, debt levels that soared out
of control prior to the Great Financial Crisis of 2008 led to reduced spending by households
burdened with payments they could not afford (McCarthy & Steindel, 2007b). The proper
amount of consumer credit options is needed for an economy to progress, but moderate levels of
household debt have been seen to produce the largest amounts of long term spending (Dynan,
2012).
6
Statement of the Problem
Downturns in macroeconomic trends have historically been followed by decreases in
consumer spending (Gaber, Gruevski, & Gaber, 2013). This was very evident during the great
financial recession of 2008 when unfavorable shifts in macroeconomic trends led to decreased
consumer spending, and eventually led to the largest recession in the United States since the
Great Depression (Catte, Cova, Pagano, & Visco, 2011). Success in the travel and tourism
industry is driven by consumer spending of excess cash that is more readily available during
times of macroeconomic stability (Cantor & Rosentraub, 2012).
Researchers have demonstrated the effects of macroeconomic downturns on consumer
spending in retail industries, including grocery stores, when metrics like consumer confidence,
interest rates, unemployment rates, stock market values, and consumer credit index decline (Ma,
Ailawadi, Gauri, & Grewal, 2011). However, there are industries, such as regional theme parks,
that are separate from other retail, travel and tourism destinations because of their unique
variables (Salamat & Banik, 2013a). The regional theme park industry has distinct
characteristics, goals, and revenue drivers that differ from the standard travel and tourism
category (Salamat & Banik, 2013a).
The specific problem is that consumer discretionary spending is impacted by
macroeconomic trends, and although there is research on the impacts of macroeconomic trends
in relationship to various industries, there is no current information on how changes in
macroeconomic trends impact regional theme park attendance and overall revenue results. This
lack of information impedes the industry’s ability to develop strategies to combat changes in
consumer behaviors at regional theme parks when macroeconomics trends shift (Cornelis, 2011).
If this research is not done, regional theme parks will not know how to plan for and react
7
properly to changing macroeconomic conditions, and would most likely underperform due to this
inability.
Purpose of the Study
The purpose of this quantitative ex post facto study was to understand how
macroeconomic indicators including consumer confidence index, stock market values, interest
rates, unemployment rates, and the consumer credit index impacted regional theme park
attendance and revenue during times of different macroeconomic conditions. The independent
variable for this study was publicly reported consumer confidence indexes, interest rates,
unemployment rates, stock market trends, and the consumer credit indexes during the timeframes
evaluated. The dependent variables were the attendance and revenue performance for all parks
at both Six Flags and Cedar Fair Entertainment. The research was conducted using annual data
spanning 2007-2012, specifically analyzing the correlation of these metrics to macroeconomic
trends for each year evaluated. Annual financial reports from both Six Flags and Cedar Fair
Entertainment was used to gather the needed attendance and revenue performance metrics as the
dependent variables for each year evaluated. Multiple regression analysis was used to conduct
statistical analysis on regional theme park attendance and total revenue performance during times
of macroeconomic shifts on an annual basis. This data was gathered from 2007-2012 and
evaluated on a quarterly or year by year basis.
Research Questions
The following research questions provide the needed bridge between the statement of the
problem and the research purpose in addition to expanding on the details of how the research will
be conducted. Consumer spending has been shown to adjust to macroeconomic trends among
the general travel and tourism industry (Cantor & Rosentraub, 2012). Research conducted for
8
the total travel and tourism industry cannot be generalized to the regional theme park industry
because no other industry is as reliant on consumer’s ability to change their consumption, or
attendance, patterns on such a consistent basis (Cantor & Rosentraub, 2012). This ability for
customers to change plans throughout the operating season impacts both attendance and revenue
numbers for regional theme parks (Bakir & Baxter, 2011). Due to the major impact that
consumer spending has on regional theme park attendance and revenue performance, the ability
for regional theme parks to forecast consumers’ spending at their parks based on macroeconomic
trends will provide stakeholders with the information needed to plan and proactively manage
times of changing macroeconomic conditions. The research questions and hypothesis for this
study are below.
Q1. To what extent, if any, is there a relationship between merging all five predictive
variables (consumer confidence index, stock market values, interest rates, unemployment rates,
and the consumer credit index) together and attendance at Cedar Fair theme parks?
Q2. To what extent, if any, is there a relationship between merging all five predictive
variables (consumer confidence index, stock market values, interest rates, unemployment rates,
and the consumer credit index) together and revenue performance at Cedar Fair theme parks?
Q3. To what extent, if any, is there a relationship between merging all five predictive
variables (consumer confidence index, stock market values, interest rates, unemployment rates,
and the consumer credit index) together and attendance at Six Flags theme parks?
Q4. To what extent, if any, is there a relationship between merging all five predictive
variables (consumer confidence index, stock market values, interest rates, unemployment rates,
and the consumer credit index) together and revenue performance at Six Flags theme parks?
9
Q5. To what extent, if any, is there a covariance within the predictive variables consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index and attendance?
Hypotheses
H10. There is no relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and attendance at Cedar Fair theme parks at a statistically significant level.
H1a. There is a relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and attendance at Cedar Fair theme parks at a statistically significant level.
H20. There is no relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and attendance at Six Flags theme parks at a statistically significant level.
H2a. There is a relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and attendance at Six Flags theme parks at a statistically significant level.
H30. There is no relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and revenue performance at Cedar Fair theme parks at a statistically
significant level.
H3a. There is a relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
10
credit index) together and revenue performance at Cedar Fair theme parks at a statistically
significant level.
H40. There is no relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and revenue performance at Six Flags theme parks at a statistically
significant level.
H4a. There is a relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and revenue performance at Six Flags theme parks at a statistically
significant level.
H50. There is no significant covariance within at least one of the predictive variables
consumer confidence index, stock market values, interest rates, unemployment rates, and the
consumer credit index.
H5a. There is a significant covariance within at least one of the predictive variables
consumer confidence index, stock market values, interest rates, unemployment rates, and the
consumer credit index.
Nature of the Study
The purpose of this non-experimental quantitative method of inquiry, utilizing ex post
facto quantitative research, is to understand how the consumer confidence index, stock market
values, interest rates, unemployment rates, and the consumer credit index impact regional theme
park attendance and revenue performance. The data will be gathered using publicly distributed
records consisting of annual totals for both sets of dependent variables. Having both sets of
confirmed data will allow the researcher to use correlation analysis to determine the relationship
11
between both sets of variables. Multiple research articles produced by Carter (Carter, 2014),
along with the previously mentioned study from Jarde (Jarde et al., 2012) in 2012 has validated
the use of multiple regression analysis to evaluate multiple variables against financial
performance metrics to understand correlations or a lack thereof between the variables (Carter,
2015). The use of this method within other financial studies also gives validity to the analysis
method used in this study (Carter, 2014). To understand how the consumer confidence index,
stock market values, interest rates, unemployment rates, and the consumer credit index impact
regional theme park attendance and revenue performance, the following study will use the
interval values of regional theme park attendance and revenue performance as the dependent
variables. Cedar Fair Entertainment and Six Flags annual reports will serve as reliable and
accurate sources for the data related to both dependent variables. Likewise, the independent
variables that consist of the consumer confidence index, stock market values, interest rates,
unemployment rates, and the consumer credit index are also interval values ranging in values
depending upon the specific independent variable. Each of the independent variables is
published nationally on at least an annual basis generating reliable and accurate data for the
study.
Significance of the Study
With a combined total of approximately 85,000 employees across most regions in the
United States, the financial success of both Six Flags and Cedar Fair Entertainment is not only
crucial to their shareholders but is a major contributing factor to the economy in the areas around
their theme parks. With consumer spending continuing to be impacted by macroeconomic
trends, the lack of detailed research regarding information on how changes in macroeconomic
trends impact regional theme park attendance and overall revenue results is concerning. Without
12
the proper knowledge and research, the industry’s ability to develop strategies in order to combat
changes in consumer behaviors at regional theme parks when macroeconomics trends shift will
continue to be limited (Cornelis, 2011). These limitations will not only impact the shareholders
of each company in a negative way but will also impact the employees and surrounding
economies that are involved with each park. This study will result in three major contributions to
the regional theme park industry.
First, the study will provide regional theme park operators with the needed information to
predict consumer spending and visitation patterns based on macroeconomic shifts in the short
term. This will allow the operators the ability to staff and prepare appropriately for the guests
and their needs before the activities occur. The ability to predict consumer behavior in this
manner will generate efficiencies from a labor perspective, but will also generate additional
revenue for each company due to their preparation to increased attendance and spending when
macroeconomic trends indicate so. Second, the research will be useful in giving each company’s
guests the proper promotions during a given timeframe based on the guests’ likely level of
spending. Having the right product produced, advertised, and delivered to the consumer at the
right time will increase sales and revenues for the industry. Third, the research will provide
long-term spending expectations for each location within a company’s portfolio, giving them the
ability to plan the appropriate capital investments or savings at the right times. These changes
will not only increase the revenue totals for the industry, but will also provide a better customer
experience for the guests due to proper planning, product placement, and experience
development.
13
Definition of Key Terms
An essential need for the application of this study is to fully understand the key terms
associated with the study. The source for all the definitions provided with be identified by the
citations. Whenever possible the terms were communicated in an understandable manner for
persons not familiar with the specific financial terminology used throughout the study.
Consumer Confidence Index. For this study, consumer confidence is a consumer’s
expected output of income based on their expectation of future wealth (Gomes, 2010). The main
quantitative measure of consumer confidence in the United States, the Consumer Confidence
Index (CCI), is based on a monthly survey of 5,000 households that is conducted by the
Conference Board, an independent research association (American Britannica, 2015). The CCI is
closely watched by businesses, the Federal Reserve, and investors (American Britannica, 2015).
New York Stock Exchange. For this study, the New York Stock Exchange (NYSE) is
one of the world’s largest marketplaces for securities and other exchange-traded investments
(American Britannica, 2015). Most common households do not have large amounts invested in
the NYSE, but considering households in the top one percent of net worth hold one-third of all
assets, most consumers view the performance of the NYSE as an indicator for future overall
economic performance (Poterba, 2000).
Interest Rates. For this study, interest rates are defined as the percentage usually on an
annual basis that is paid by the borrower to the lender for a loan of money (American Britannica,
2015). The United States government controls the rate at which money is lent from the
government to lending organizations, thus impacting the amount of interest charged to
consumers (Fullwiler, 2007).
14
Unemployment Rate. For this study, the unemployment rate is defined as the
percentage of the population with the condition of one who can work, actively seeking work, but
unable to find any work (American Britannica, 2015). The study will evaluate the impact of
unemployment rates on other areas of the economy and discuss the known impact of financial
legislation on the unemployment rate itself (Gatti, 2009).
Consumer Credit Index. For this study, consumer credit is defined as the amount of
short- and intermediate-term loans used to finance the purchase of commodities or services for
personal consumption or to refinance debts incurred for such purposes (American Britannica,
2015). Some theories have contributed excessive consumer credit availability as a driving factor
of the financial crisis of 2009. This same study also indicated that younger consumers use credit
to maintain their standard of living, thus keeping the economy moving during slower times of
economic growth (Schooley & Worden, 2010).
Revenue. Revenue, in economics, is the income that a firm receives from the sale of a
good or service to its customers (American Britannica, 2015). Revenue totals to be discussed in
the study will be the result of admission, games, merchandise, and food purchases at the two
regional theme parks being evaluated (Salamat & Banik, 2013b).
Summary
Consumer spending is a highly researched topic and has been evaluated for a variety of
industries. Researchers have analyzed the relationship between macroeconomic conditions and
spending by consumers specifically in the retail industry (Abaidoo, 2014). Since the Great
Financial Crisis of 2008, the focus among scholars on consumer spending has continued to
15
expand. Consumer confidence trends based on lowered income expectations have been
connected to spending restraint by consumers in the short run (Gomes, 2010). This same study
concluded that the reaction to these short-term spending shifts should not cause an overreaction
by businesses that would drastically change long-term strategies used in planning processes
(Gomes, 2010).
To better understand these occurrences, the theory of planned behavior is used to evaluate
the influence of consumer’s knowledge, beliefs, and feelings related to a product (Bhuyan,
2011). The theory of planned behavior commonly assumes that an individual’s attitude towards
a behavior, based on previous knowledge, feelings, or beliefs related to this behavior will
determine their actual behavior (Bhuyan, 2011). The specific problem is that consumer
discretionary spending is impacted by macroeconomic trends, and although there is research on
the impacts of macroeconomic trends in relationship to various industries, there is no current
information on how changes in macroeconomic trends impact regional theme park attendance
and overall revenue results. This lack of information impedes the industry’s ability to develop
strategies to combat changes in consumer behaviors at regional theme parks when
macroeconomics trends shift (Cornelis, 2011).
The purpose of this quantitative ex post facto study is to understand how macroeconomic
indicators including consumer confidence index, stock market values, interest rates,
unemployment rates, and the consumer credit index impact regional theme park attendance and
revenue during times of different macroeconomic conditions. The research will be conducted
using annual data spanning 2007-2012, specifically analyzing the correlation of these metrics to
macroeconomic trends for each year evaluated. Annual financial reports from both Six Flags and
16
Cedar Fair Entertainment will be used to gather the needed attendance and revenue performance
metrics as the dependent variables for each year evaluated.
17
Chapter 2: Literature Review
The purpose of the quantitative ex post facto study is to understand how macroeconomic
indicators including consumer confidence index, stock market values, interest rates,
unemployment rates, and the consumer credit index impact regional theme park attendance and
revenue during times of varying macroeconomic conditions. The independent variable for this
study will be publicly reported consumer confidence indexes, interest rates, unemployment rates,
stock market trends, and the consumer credit indexes during the timeframes evaluated. The
dependent variables will be the attendance and revenue performance for all parks at both Six
Flags and Cedar Fair Entertainment. As such, the following literature review focuses on the
theory of planned behavior’s relationship to consumer spending and its impact on regional theme
parks’ overall success. A large focus of this literature review covers relevant scholarly research
on the topics of macroeconomic trends, such as the consumer confidence index, stock market
values, interest rates, unemployment rates, and the consumer credit index. These topics and their
impact on consumer spending will also be discussed in detail. Finally, the review of literature
will cover the relationship between these macroeconomic indicators and consumer spending at
regional theme parks and the resulting impact on attendance and revenue performance.
Documentation
Literature searches for this research were conducted using various databases. Each
provided peer revived scholarly research that evaluated consumer spending trends using
regression models and other statistical analysis methods. Specific studies related to the theory of
planned behavior were also used for this research. Lastly, searches were conducted among
official government websites providing macroeconomic data during the timeframe evaluated.
18
Theory of Planned Behavior
When thinking about consumer behavior recent research evaluates a common assumption
revolving around the belief that individuals are rational and behave in a reasonable manner when
making purchase decisions (Bhuyan, 2011) . Research has concluded that in some instances,
rational behavior is overruled by one’s desires related to a product or activity. To better
understand these occurrences, the theory of planned behavior is used to evaluate the influence of
consumers’ knowledge, beliefs, and feelings related to a product (Bhuyan, 2011). The theory of
planned behavior commonly assumes that an individual’s attitude toward a behavior, based on
previous knowledge, feelings, or beliefs related to this behavior will determine their actual
behavior (Bhuyan, 2011). A recent study titled “Do consumers’ attitudes and preferences
determine their FAFH behavior? An application of the theory of planned behavior” concluded
that a consumer’s attitude toward an activity or product drove the level of engagement or
amounts consumed (Bhuyan, 2011).
Another recent study examined wine festivals using the theory of planned behavior
focusing on the intentions of consumers to purchase an item or to engage in an activity (Duarte
Alonso, Sakellarios, & Cseh, 2015). This study discussed consumers’ increased effort to perform
an activity or to make a purchase based upon their intention to do so beforehand (Duarte Alonso
et al., 2015).
The research cited that when consumers could choose in their own timeframe to perform
or to not perform a behavior, they were more likely to do so (Duarte Alonso et al., 2015). This
research concluded on four main factors that drove consumers to behave in a certain way. These
factors included “commitment and perceived importance, consumption and entertainment,
19
attendance and discover, and joining others” (Duarte Alonso et al., 2015). Three-hundred and
eight questionnaires were used to measure attitudes towards the behavior, perceived behavioral
control, and subjective norms on participants attending the Derbyshire Food and Drink Fair
(Duarte Alonso et al., 2015). Of the three hundred and eight useable responses, significant
differences related to age, gender, and distance traveled to the fair were noted (Duarte Alonso et
al., 2015). The study noted potential outcomes of the study as being beneficial to the fair
industry looking to understand the motivations driving their guest within each group studied
(Duarte Alonso et al., 2015). The study also utilized the theory of planned behavior to examine
the factors that led to guest attending these festivals, thus expanding the development of the
theory of planned behavior (Duarte Alonso et al., 2015). Using this theory for their study, the
research indicated consumers’ attitudes towards this behavior, their perceived level of social
acceptance towards this behavior, their perceived level of effort that is needed to engage in this
behavior, and the past experiences of the consumers as predictors for engagement in this activity
(Duarte Alonso et al., 2015). The major demographic takeaways from the study includes the
gender mix with females accounting for sixty-one percent of the population and the age group of
above forty-six making up over sixty-seven percent of participants(Duarte Alonso et al., 2015).
While the conclusion of the study did backup the four stated factors as the driving influence on
the planned behaviors of the study, they also found a unique trend among the younger
participants in the study. This group had a higher expectation of future visits to these types of
events looking for new experiences with non-traditional offerings rendering information that
could be useful to organizing such events in the future (Duarte Alonso et al., 2015).
Recent research was conducted examining the growth of specialty coffee consumption.
The study analyzed consumers’ behavioral intentions towards specialty coffee using the theory
20
of planned behavior (Merwe & Maree, 2016). The study evaluated attitudes, subjective norms,
and perceived behavioral control in relationship to consumers’ preferences and intentions
towards the consumption of specialty coffee (Merwe & Maree, 2016). Coffee enthusiasts who
were over the age of eighteen residing in the major city centers of South Africa where chosen for
this study (Merwe & Maree, 2016). Surveys were distributed to known connoisseurs and to
coffee cafés that distributed the surveys across their customer base.
Results of the surveys indicated that cultural norms were an important factor in the
decision processes related to specialty coffee consumption (Merwe & Maree, 2016).
Considering that most consumption occurred in the presence of other individuals, these results
were not surprising. Positives attitudes toward this activity also encouraged future intentions to
purchases specialty coffees (Merwe & Maree, 2016). While attitude was the least important
factor, perceived quality, taste, and health benefits were the main factors associated with
consumers’ attitudes towards the product. Contrary to previous research, age did not play a role
in predicting consumption, with younger consumers drinking more specialty coffees when
compared to the past (Merwe & Maree, 2016) . The study concluded that retailers must adapt to
the changing consumer by not only providing the quality and variety that is expected, but by also
capturing the younger consumers as they move into adulthood. Promoting the social benefits of
status related to the consumption of specialty coffees has been a successful tactic in achieving
these goals (Merwe & Maree, 2016).
Another study utilizing the theory of planned behavior to evaluate consumer attitudes,
norms, and perceived behavioral controls related to the consumption of sugar-sweetened
beverages, water, and artificially sweetened beverages. This study used the homogenous
sampling strategy focusing on groups of individuals in the southwest region of Virginia (Zoellner
21
et al., 2012). This sampling group was chosen because of the region’s high diabetic rate among
it’s residents. This region also has higher than average rates of obesity and has been recognized
by the federal government as a medically underserved area (Zoellner et al., 2012). The research
used frequencies, means, standard deviations, Chi-squared, and one-way ANOVA’s to analyze
the data with a hybrid deductive and inductive qualitative analysis approach. The results
indicated four major themes that drove consumption across all categories evaluated (Zoellner et
al., 2012). Taste, availability, habit, and cost emerged as the leading factors related to
consumption across the different categories. While noted as influential to the behavior, health
impacts, water quality, and normative beliefs among participants’ peers and doctors had smaller
impacts on the decision-making process (Zoellner et al., 2012). A previously released study
related to the theory of planned behavior among Dutch adults listed satisfaction, health, social
influences, habit, availability, and awareness as factors leading to fruits and vegetables (Zoellner
et al., 2012). This study was noted based on its stark contrast to the study conducted in Virginia.
Consumer Spending
Economists refer to consumer spending as the goods or services bought by a household to
fulfill their needs and wants, through a variety of suggested and implemented fiscal policies
(Nnadi, 2011). Since the great recession of 2008, economists and researchers have been
focusing on ways to reduce the decline of consumer spending. Recent research determined that
interest rates, annual inflation, annual earnings increases, and mortgage rates affect consumer
spending in the most direct manner (Nnadi, 2011). This study developed a consumer spending
model to assess the impact of fiscal policies related to bank rates, inflation, earnings, and
mortgage rates on consumer spending (Nnadi, 2011). Four variables including the bank or
22
interest rate, annual inflation rate, annual earnings increase, and mortgage rate were used to build
the model comparing these factors to consumer spending and the behavioral patterns during the
great financial crisis of 2008 (Nnadi, 2011). The retail price index was used as a standard for
measuring the amount spent by households on consumables in the study (Nnadi, 2011). The
study included findings concluding that government entities should generate additional economic
activity through the stabilization of inflationary trends (Nnadi, 2011). This stabilization of
inflationary trends will generate increased consumer spending through lower interest rates,
increased earnings, and lower mortgage rates (Nnadi, 2011).
Macroeconomic indicators including consumer confidence, stock market values,
unemployment rates, and the consumer credit index have also been shown to impact consumer
spending (Abaidoo, 2015).With consumer spending accounting for seventy percent of the overall
gross domestic product prior to the recession of 2008 (Yerex, 2011b), this topic continues to
grow as a research and discussion topic among scholars and legislators alike. Prior to the great
financial crisis of 2008, consumption levels were at a record high. Research concluded that
normal trends of spending would not occur soon without indicating a specific timeframe for
normal trends to resume (Yerex, 2011b). To evaluate these consumption trends, the research
used a conceptual consumer spending model (Yerex, 2011b). On one side of the equation, the
model included three main components consisting of personal wealth, disposable income, and
savings (Yerex, 2011b). On the other side of the equation, investments, borrowings, withdrawals,
and expenditures were considered (Yerex, 2011b). These categories capture all spending
activities both incoming and outgoing (Yerex, 2011b). A combination of the labor index and the
Department of Commerce Retails and Food Services Sales Survey were used to evaluate
spending trends (Yerex, 2011b). Four phases were identified leading up to the financial crisis of
23
2008 and the years preceding it (Yerex, 2011b). The first phase between late 2006 and early
2007 saw continued increases in retail sales and labor indexes alike (Yerex, 2011b). During
2007, in phase two, a shift in labor indexes happened while sales trends continued to rise, putting
each index on opposite sides of the trend (Yerex, 2011b). As the labor index, would have
predicted, the sales trends dropped in 2008, in phase three, catching up to the labor index levels
by the end of the corresponding year (Yerex, 2011b). During the fourth phase, in 2009, the labor
index slowly rose with the retail sales trends following which has been the case since (Yerex,
2011b). The dramatic changes in the economy over the past fifty years were used as an example
to underscore the likelihood of normalization along with the examples of items that led to
increased spending prior to the Great Financial Crisis. Those include overvalued homes and
easily accessible credit (Yerex, 2011b). Other research evaluated consumers’ intent to change
spending patterns during times of economic change using the theory of planned behavior
(Chambers, Benibo, & Spencer, 2011). One such study used a survey of four hundred and fifty-
eight faculty and students from South Texas University, ensuring that faculty from all colleges at
the university and all levels of students were included to prevent bias. When using the theory of
planned behavior to analyze the relationship between one’s attitude and the influence of the
norms around everyone, the impact of each on participants’ activities varied. During times of
financial crisis, consumers’ attitudes had a significant impact on their decision to move money,
but had no impact on their decision to change jobs (Chambers et al., 2011). While the norms
around these consumers did have a small influence on their thoughts about changing jobs, the
norms had the greatest impact regarding where consumers placed their money (Chambers et al.,
2011). The research concluded that when making decisions about where to allocate funds,
consumers are influenced more by those around them, or the norm, as compared to other large
24
decisions where personal attitude or experience is the driving factor (Chambers et al., 2011). To
better predict future spending trends, current research has evaluated the impact of pricing,
income, and inflation on consumer spending trends (Nnadi, 2011). This research created a
consumer spending model based on the retail price index and the Hodrick and Prescott filter as
the smoothing coefficient (Nnadi, 2011). The study also used the assumption that the retail price
index is dependent on the bank rate, inflation, earning increases, and mortgage rates (Nnadi,
2011). The study noted that as spending is increased, consumer confidence will also rise, driving
bank rates up with inflationary pressures (Nnadi, 2011). While moderate pricing increases and
subtle income increases played a small role in keeping consumer spending levels flat after the
recession subsided, inflation trends also played a large role reducing the amount that each dollar
can buy (Nnadi, 2011). This has caused governments to implement policies encouraging the
stabilization of inflationary pressures to encourage future consumer spending growth (Nnadi,
2011). The research concluded that stabilization of inflationary trends played a larger role than
earnings increases or mortgage rate reduction, supporting the push for government regulation of
inflationary trends (Nnadi, 2011). It was shown that interest rates and inflation are highly
correlated to the retail price index and consumer spending overall (Nnadi, 2011).
Consumer spending as a driving factor relating to unemployment has been greatly
overlooked. A recent study examined the impact on employment when consumer spending
declines (Barello, 2014). During the great financial crisis, over 3.2 million jobs were lost due to
reduced consumer spending (Barello, 2014).This accounted for over a third of the total job loss
during that timeframe (Barello, 2014). Over half of the remaining jobs were related to gross
private investment with exports, and state or local governments accounting for the remaining five
and four percent respectively (Barello, 2014). In contradiction, during the recovery, jobs related
25
to consumer spending recovered at a faster rate compared to their counterparts(Barello, 2014).
This employment growth fueled by consumer spending is expected to continue at a moderate rate
including increased expenditures on labor-intensive services like healthcare (Barello, 2014).
With consumer spending accounting for seventy one percent of the United States’ gross
domestic product and just over fifteen percent of the entire global economy, understanding the
impact of consumer spending is crucial for any business (Barello, 2014). To understand the
correlation, the study created an equation to determine how much output supports consumer
demand, and then calculated production that is translated into employment using labor trends,
productivity trends, and current employment ratios (Barello, 2014).
Over the next several years, the research concluded that consumer spending would
continue to be a driver of economic growth. While the overall economy is predicted to grow at a
slower rate versus historical norms, consumer spending is expected to trend at a similar rate
when compared to the overall economy (Barello, 2014). Consumers are predicted to be
responsible for over seventy percent of the United States gross domestic product by 2022.
Ninety-five percent of the jobs related to this spend are expected to be in the service industry,
with over half of the growth expected to come from the health care and social assistance sectors.
It is crucial for all industries to understand these trends to understand the impact of lower
consumer spending throughout the entire economy (Barello, 2014).
Stress has also been linked to consumer savings and consumer spending. When
consumers are threatened by stressful situations or environments, they use their spending to take
back control (Durante & Laran, 2016). Consumers either place more money into savings to
prepare for economic downturns, or utilize their spending to purchase perceived essentials to
26
gain control during stressful situations. This study’s purpose was to examine the increased
willingness of consumers to spend or save during times of stress (Durante & Laran, 2016). To
gather the data, multiple surveys were used to generate participant responses. These responses
were analyzed using analysis of variance between stress and the control that was perceived
through spending (Durante & Laran, 2016). The research concluded that stress led more
consumers to save than to spend. The spending that did take place during these stressful times
was found to be more strategic, revolving around products that were perceived to be essential to
the current lifestyle of the consumer (Durante & Laran, 2016). The research also contributed to
the expanding amount of literature around the interplay of environmental and physiological
factors that are influencing consumer decision making. For companies of all types to be
successful in the future, understanding this dynamic will be crucial when evaluating consumer
spending decisions (Durante & Laran, 2016).
Regional Theme Parks
Multiple types of amusement parks exist across the county including attraction parks,
theme parks, safari parks, aquatic parks, and recreation parks (Salamat & Banik, 2013b). These
parks have been designed as permanent destinations for the public to use as entertainment hubs
(Salamat & Banik, 2013b).These destinations are also used for educational purposes and are
visited by locals and tourists alike (Salamat & Banik, 2013b). Regional theme parks, also called
amusement parks and recreational parks, are designed to attract consumers that want to spend a
specific amount of time in an environment that creates surroundings of enjoyment and new
experiences (Salamat & Banik, 2013b). These parks typically consist of rides or attractions that
revolve around a central theme (Salamat & Banik, 2013b). Theme parks are typically an outdoor
attraction, a specific visitor destination, require an admission fee, and are designed around the
27
needs of the consumers with the focus being on their entertainment value versus their educational
value (Salamat & Banik, 2013b). To be successful, amusement parks must contain a wide range
of options and attractions, have unique qualities on an ongoing basis, generate new innovative
features on an annual basis, continue the theme from year to year as new elements are
introduced, integrate environments that match the location of the park, properly manage lines
and capacities, operate sound infrastructures, and entertain guests regardless of the weather
(Salamat & Banik, 2013b). Guests also want an environment that allows them to escape the
normal distractions of life on a temporary basis. This experience should be interactive while
creating an emotional attachment to the brand associated with an expectation of high quality
(Salamat & Banik, 2013b). Most importantly, theme parks must design attractions that exceed
the highest safety and security standards while employing a well-trained and highly motivated
staff (Salamat & Banik, 2013b). To be successful, amusement parks must have the proper market
strategy. The different strategies in the industry include the low cost, differentiation, or focus
strategy (Salamat & Banik, 2013b). While the four P’s including product, place, price, and
promotion are a consistent piece of this strategy, physical facility and procedure are included
when discussing amusement parks (Salamat & Banik, 2013b). When designing marketing
aspects for the consumers, the proper design of each park should relay the marketing message
being delivered outside of the park (Salamat & Banik, 2013b). With the seasonality of the
business being a constant, amusement park operators must have efficient procedures in place to
maximize the use of their properties during peak business days, while having a similar
proficiency during off peak times to complete necessary tasks related to maintenance and service
across the parks (Salamat & Banik, 2013b).
28
Consumer spending is a key component to the success of many businesses and industries
across the globe (Heim, 2010a), and is a key ingredient for success in the regional theme park
industry (Van Oest et al., 2014). Information related to factors that may increase or decrease
consumer spending is quite valuable to the regional theme park industry due to the impact of
consumer spending on the industry’s attendance and revenue performance totals (Salamat &
Banik, 2013a). However, stakeholders do not currently have enough information to generate
strategies that will combat consumer spending trends that are impacted by macroeconomic
variables. Other research evaluated how the theory of planned behavior explained consumers’
allocation of money during such economic crises (Chambers et al., 2011)
With many considering attendance at an amusement park to be a luxury item or event,
studies around luxury purchases remain relevant to studies regarding the regional theme park
industry. Using the theory of planned behavior, a study identified self-directed pleasure, superior
performance, acquisition, and self-actualization as the motivating factors for purchasing a luxury
item (Jain, Khan, & Mishra, 2015). Culture, values, attitudes, and cultural behaviors were
identified as influences on consumer’s behavior patterns when considering luxury items (Jain et
al., 2015). The study also identified signals of power and position, impressing others, conformity
to a group, and non-conformity as socially motivating factors for consumers when making a
luxury purchase decision (Jain et al., 2015). Intrinsic or personal factors were identified as the
driving forces behind one’s desire to purchase luxury items along with consumer’s desire to
express themselves (Jain et al., 2015). The research concluded that one’s feelings and personal
attitude around a brand had a large effect on a luxury purchase decision (Jain et al., 2015). Four
main motivating factors were identified including, pleasure gained from the consumption
experience, the functional or quality value of the experience, materialistic satisfaction related to
29
ownership, and the perception of one’s self when purchasing such luxury items (Jain et al.,
2015). From a norm perspective, or the feeling of society about a brand, consumers valued this
perspective as well when making the same purchase decisions (Jain et al., 2015). Motivating
factors related to these extrinsic factors include the need for status, the need to impress others, a
desire to belong to a group, or a uniqueness value that does not conform to the norm (Jain et al.,
2015). While both sets of factors are key to the behavior process, consumers’ income or ability
to purchase the item must be considered (Jain et al., 2015). The consumer’s attitude towards a
product or behavior was noted as the first conclusion in the study. This attitude towards a
product may exceed the desire for the actual product if the feeling is significant (Jain et al.,
2015). Said another way, a positive or negative attitude toward a product or company may be
the deciding factor related to consumer purchases of luxury items regardless of the product being
sold (Jain et al., 2015). The second major conclusion revolved around consumers’ perception of
people’s attitudes towards them if they made the luxury purchase (Jain et al., 2015). Even adults
are influenced greatly by the social pressure to engage activities or purchases (Jain et al., 2015).
Lastly, the study concluded that a consumer’s ease in performing the behavior or purchase was
also a driving factor in the decision process. If income levels are a deterrent to purchasing
goods, marketing or financing activities need to be in place to overcome this obstacle (Jain et al.,
2015).
As with other industries, consumer spending is a key driver to the success of regional
theme parks on an annual basis (Salamat & Banik, 2013b). A recent study in Europe noted the
lack of a model for return on investment related to new attractions added to regional theme parks
(Van Oest et al., 2014). The study generated a model using 25 years of data from a German
theme park to create a predictive model that would estimate future attendance in relationship to
30
new attraction investments. The model indicated five factors that drive amusement park
attendance including attractions, competition, seasonality, price, and macroeconomic factors
(Van Oest et al., 2014). With the impact of a new attraction being the focus of the study, this is
the main factor evaluated in the model with the other factors used as controls to negate their
impact on the study (Van Oest et al., 2014). Saturation is also considered when multiple
attractions are added in a similar timeframe (Van Oest et al., 2014). In conjunction with their
model, twenty-four years of data related to attraction investments and attendance performance
was evaluated for the German theme park used in the study. The results indicated an average
return on investment of one-hundred and thirteen percent for each attraction built (Van Oest et
al., 2014). There was a significant decline in the return of similar attractions in subsequent years
compared to earlier versions of similar attractions parks (Van Oest et al., 2014). In conclusion,
when all things were equal, thrill attractions drove higher returns when compared to themed rides
(Van Oest et al., 2014). When saturation of thrill rides became evident, themed ride investments
produced better results, adding variety for guests and meeting the needs of different demographic
groups (Van Oest et al., 2014). In contrast to the trend in America to build the largest and fastest
new attraction, the study found that multiple attractions over the course of several years had a
larger impact of nullifying the impact of adverse weather, versus one large new attraction (Van
Oest et al., 2014). While this research did contribute to predicting attendance in relationship to
new investments at regional theme parks, the study did not generate a model to predict
attendance trends during times of macroeconomic shifting.
Regional theme parks generate 80% of their attendance and revenue totals in the 2nd and
3rd quarters of the year between Memorial Day and Labor Day. The average number of operating
days for the industry is around 130 to 140 days on an annual basis. The key demographic for the
31
industry is people between the ages of 12-24 with families driving a large portion of attendance
and revenue. With this being the demographic, parents of young families are the decision
makers, deciding ultimately to visit or not to visit the park, and determining the spending at each
park. Limited direct competition within regional areas exists due to a $300 to $400-million-
dollar cost to build a new regional theme park with a two-year construction time-frame. While
the population and economic growth may exist in a specific area where an existing park is
located, the cost of development for a new park almost always restricts the existence of new
competition for existing parks.
With admission tickets and season passes being one of the largest drivers of revenue for
regional theme parks, their revenue implications have recently been addressed and evaluated
(Byun & Jang, 2015). The comparison of bonus offers versus discount programs was evaluated
to determine the perception and likelihood a guest would renew season passes to amusement
parks in their regions with each of these offers (Byun & Jang, 2015). The authors used national
surveys and a promotion between-subject design experiment to reach the conclusion that guests
that had renewed passes in the past were not affected by either of the promotional methods
(Byun & Jang, 2015). Two national surveys were conducted by an online research firm to one
set of respondents that had never subscribed or purchased a membership to an amusement
attraction (Byun & Jang, 2015). A second survey was sent to participants that had held a season
pass to a theme park or a botanic garden in the past (Byun & Jang, 2015). ANOVA statistical
analysis was used to evaluate the significance of the results (Byun & Jang, 2015). Guests that
had not renewed passes in the past were more likely to renew their passes if the promotion
included a bonus offer with a perceived value versus a discount promotion with a known set
value (Byun & Jang, 2015). A promotion that included a new attraction and a promotion had the
32
highest level of significance, with promotional messages only providing the lowest amount of
response for new customers (Byun & Jang, 2015). In a similar manner, when a new attraction
was combined with a renewal offer, previous guests responded at a much higher rate (Byun &
Jang, 2015). While previous guests did show some response to a discount or promotional offer
without a new attraction, new guests showed virtually no response to these offers, indicating new
attractions were crucial to gaining new customers (Byun & Jang, 2015). While this study did
address the impact of promotional types on season pass renewals, thus evaluating an attendance
driver, the research did not add to the knowledge base around revenue impacts driven by
attendance shifts during macroeconomic irregularities.
With attendance being so closely tied to revenue performance, regional theme parks are
consistently trying to enhance customer experiences to drive initial and repeat attendance. One
recent study based in Taiwan evaluated five experiential marketing strategies created to drive
guests’ willingness attend, revisit, and recommend specific theme parks (Jung, 2016). The
experiment divided the experience marketing into five categories including sense, emotion,
thinking, action and relevance.
Sense marketing was described as activities focused on vision, hearing, smelling, tasting,
and touching to create purchase motivation (Jung, 2016). Experiences created to evoke positive
feelings toward to the product or brand were labeled as emotional marketing activities.
Activities created to allow consumers to see a product or company in a new light or from a
different angle were described as thinking marketing (Jung, 2016). Getting consumers to
visualize how their lives will be changed because of consumption was referred to as action
marketing. Lastly, the linking between a consumer’s psychology, society, and culture was
described as relevance marketing (Jung, 2016).
33
The study utilized surveys completed across multiple theme parks in Taiwan. The results
of these surveys were analyzed using regression analysis, and one-way ANOVA to evaluated
statistical relevance (Jung, 2016). All five of the marketing strategies were found to influence
revisit willingness with consumption willingness being driven by action, relevance, and sense
marketing. Relevance, emotional, action, and sense marketing drove the highest
recommendation willingness among participants (Jung, 2016). Age, marital status, educational
background, income level, and family income levels also influenced which marketing strategy
was most influential. Understanding the driving factors among these survey participants will
enhance theme parks marketing strategies to grow consumer engagement (Jung, 2016).
Consumer Confidence
Consumer confidence is typically defined as the likelihood that consumer spending is to
be relatively strong or relatively weak among consumers in the general population of an
economy (Parker, Souleles, Johnson, & McClelland, 2013). Since the great recession of 2008,
the issue of consumer confidence has been a highly-investigated topic (Yerex, 2011a), with the
correlation of consumer confidence being increasingly linked to consumer spending. An
independent research firm called The Conference Board releases the consumer confidence index
monthly. The value of the index typically ranges around 60-140 with a value of 100 being an
average middle point of consumer confidence.
A recent study conducted in 2010 concluded that during times of macroeconomic
uncertainty, decreased consumer confidence led to retailers having negative viewpoints
regarding spending in the short run (Eastman, McKay, & Forehand, 2010). The study contained a
hypothesis for both sets of respondents including retailers’ and consumers’ perception of the
34
economy, retailers’ and consumers’ financial soundness, retailers’ cost of operations, consumers’
cost of living, and retailers’ and consumers’ perceptions of the upcoming holiday spending
season (Eastman et al., 2010). Surveys were used for both sets of participants to gain an
understanding of the attitudes for each group regarding the economy just prior to the great
financial crisis (Eastman et al., 2010). The questions used in the survey asked both sets of
respondents to give their opinion related to the overall soundness of the economy, soundness of
their current situation, the level of overall cost and expected holiday spending in comparison to
the previous year for all questions (Eastman et al., 2010). Using an independent T-test for both
sets of respondents, the following conclusions were derived. Both consumers and retailers had a
similar perception of the economy overall. Retailers, however, felt more financially sound when
compared to consumers leading into this time frame (Eastman et al., 2010). In a similar manner,
consumers felt that their costs had risen at a higher rate versus last year in comparison to
retailers' thoughts on the same subject. Given the previously mentioned results, it is no surprise
that consumers had a lower expectation of spending during the holiday season compared to
retailers’ expectation (Eastman et al., 2010). This was verified by the actual spending by
consumers during the following holiday spending season. Both consumers and retailers held
negative views about spending in the short term. However, the consumers’ amount of decreased
spending exceeded the retailers’ expectations (Eastman et al., 2010). Research has shown that
increases and decreases in consumer confidence have been followed by similar changes in
consumption and followed by similar changes in investments (Heim, 2010b). Research using the
theory of planned behavior also indicated the same trends, concluding that attitudes that are
influenced in the short-term drive decision making (Chambers et al., 2011). This data would
indicate that long term decisions like investments are not as impacted by shifts in consumer
35
confidence, where day to day consumer spending, on the other hand, is very tied to consumer
confidence levels (Heim, 2010b). The decision of consumers to attend amusement parks falls in
the category of day to day spending, thus tying the attendance of consumers at amusement parks
to consumer confidence.
Recently, researchers in Canada evaluated the ability of the Conference board of
Canada’s Index of Consumer Attitudes to predict consumer spending based on consumer
confidence levels at the national and regional level (Kwan & Cotsomitis, 2006). The study used
correlation analysis to examine the link between Canada’s Index of Consumer Attitudes and
household spending at both the national and regional levels (Kwan & Cotsomitis, 2006). This
index asks consumers about their thoughts around their financial position over the next year, the
general economic situation of their country over the next year, the level of unemployment rates
in their country over the next year, and their expectations around savings over the next year to
generate the results (Kwan & Cotsomitis, 2006). Correlation analysis was conducted to generate
the results of the study. The study found a high correlation between the consumer confidence
index and household spending at the national level but failed to generate the same levels of
correlation when evaluating the index to regional levels of household spending (Kwan &
Cotsomitis, 2006). While this study did determine the correlation at specific regional levels,
further research is needed to understand if a similar correlation exists between consumer
confidence indexes and regional theme park attendance and revenue performance at the national
level.
The importance of focusing on consumer spending was highlighted by a recent study
noting that seventy percent of the United States gross domestic product is generated by consumer
36
spending (Yerex, 2011a). The correlation connecting consumer confidence levels and consumer
spending was further confirmed by a recent study that connected increased consumer spending
with just the expectation of financial increase (Gomes, 2010). Multiple models were constructed
to examine consumer spending habits during times of consumer confidence change. So, even
before consumers had the money in hand, they have been shown to spend more as their
confidence levels increased through an expectation of future financial security (Gomes, 2010).
When consumers had a positive outlook on their ability to grow above normal expectations, their
spending levels increased. (Gomes, 2010).
The impact of consumer confidence on consumer spending was recently evaluated across
specific sectors. These included durable goods, semi-durable goods, and nondurable goods.
The purpose of the study was to enhance the predictability of consumer trends by enhancing the
research on this topic down to the specific sector level (Gausden & Hasan, 2016). Correlation
analysis was done on published consumer confidence levels and consumer spending trends
within the specific sectors to generate the study results. The research concluded that analyzing
these specific sectors would have given greater predictive ability during the timeframe analyzed.
With the exclusion of auto purchases, the durable goods category would have seen the greatest
impact (Gausden & Hasan, 2016).
While many studies have evaluated the impact of macroeconomic indicators on the
consumer confidence index, a recent study looks to do the inverse. The factor-augmented vector
auto regression framework was used to evaluate the data throughout the study (Kilic & Cankaya,
2016). Monthly data from January of 1994 through June of 2013 was used including the
consumer confidence index and financial data from the federal reserve. Factors gathered from
the federal reserve included the unemployment rate, federal funds rate, nonfarm business labor
37
productivity rate, adjusted gross private domestic investment rate, and the consumer price index
(Kilic & Cankaya, 2016).
The findings of the study confirm that manufacturing related variables, housing market
variables, and durable and nondurable goods are sensitive to consumer confidence changes. The
inventory to sales ratio in manufacturing shows a very strong correlation in both the short and
long term (Kilic & Cankaya, 2016). Gas and utilities show a constant correlation to the index
with services showing a larger correlation compared to durable and nondurables goods when
evaluating personal consumption to consumer confidence levels. Housing market trends did not
change versus previous research, with increases in consumer confidence intervals driving
housing market growth (Kilic & Cankaya, 2016). While a direct correlation between consumer
confidence index and macroeconomic variables cannot be confirmed, psychological factors have
been seen in the short run (Kilic & Cankaya, 2016).
Stock Market Values
While at a first glance, the stock market values may seem like a point of interest for only
the wealthy, current research has proven the connection between the stock market and total
consumer spending (Sum, 2014). To generate their findings, the study used regression analysis
estimating the relationship of business confidence, consumer confidence, and the stock market’s
performance (Sum, 2014). The study found that with every unit of change in the business
confidence level the stock market also increased one and a half percent. When consumer
confidence levels change by one unit, the stock market has an even greater response, increasing
by over four percent (Sum, 2014). Consumers see the stock market as a leading indicator of
future economic activity, thus giving the consumer the expectation of financial growth or decline
(Hsu, Lin, & Wu, 2011a). The same research indicated a decline in consumer investments during
38
downshifts in the stock market as an initial reaction, further enhancing the connection between
consumer spending and stock market values (Hsu, Lin, & Wu, 2011a). This research used the
Granger-causality framework to investigate the causal relationship between consumer confidence
and the stock market (Hsu, Lin, & Wu, 2011a). Monthly data related to consumer confidence
and stock market levels from twenty-one countries over a period of eight years was gathered for
the study (Hsu, Lin, & Wu, 2011a). The study concluded that a strong cross-sectional
correlation exists between the two variables. Not only will consumer confidence levels increase
as stock market values rise, but more individuals will be willing to invest in the stock market
when their confidence levels are increased (Hsu, Lin, & Wu, 2011a).
Age of consumers has also been linked to the amount of short term and long term risk
consumers are willing to accept in both their spending and investing (Johnson & Naka, 2014).
The research also indicated a stronger response from consumers related to negative trends versus
positive trends in the stock market (Johnson & Naka, 2014). Greater changes resulted from
negative moving in both trends, indicating a larger downside risk versus an opportunity for
upside when evaluating these two variables (Johnson & Naka, 2014). While the research did
indicate a stronger risk aversion from older consumers, the ability to predict consumer behavior
from this connection was only useful for short term decisions and did not produce a correlation
when looking at long term spending decisions (Johnson & Naka, 2014). Because of this finding,
the study advised for the risk calculation to be included regarding the demographic mix of the
investor base when generating future models (Johnson & Naka, 2014). As information becomes
more easily accessed by more people, the impact of global opinions continues to be a larger
impact on consumers making personal decisions with money allocation (Chambers et al., 2011).
39
Another specific research indicated a similar result in consumer spending during times of
stock market decline. The example was given from early 2009 when the value of stocks plunged
by as much as fifty percent versus their highs a few years earlier as compared to real estate
values that had only fallen by twenty-five percent at this point (Cooper & Dynan, 2013). With
many younger consumers using credit for daily purchases, the availability of credit has a higher
impact on their spending in comparison to actual wealth growth. While stocks may not be rising,
when home values do the opposite additional credit availability is generated, spurring these
individuals to spend (Cooper & Dynan, 2013). Stock market values are used more as an
indicator of future wealth, spurring future spending, especially for those individuals that own
stock (Cooper & Dynan, 2013). One other impact of rising stock market values has been
increased contributions to one’s retirement plan. When this is done, daily spending is reduced to
contribute the funds to the retirement account, decreasing the amount of spending on consumer
goods during a time of actual wealth growth among stockholders (Cooper & Dynan, 2013). As
the population ages, this trend should reduce as older consumers begin to spend their profits
instead of placing them in savings, due to the limited timeframe remaining for them to spend
these funds (Cooper & Dynan, 2013). One other factor that has modified these trends has been
the level of debt held by consumers. During times of downward trends, large amounts of debt
are used to maintain consumption levels. As the situations improve, extra funds or profits are
used to decrease consumer’s levels of debt back down to desired levels, thus decreasing the
amount of spend into the economy (Cooper & Dynan, 2013). Consistent with other related
studies, this research connected the expectation of future wealth to consumer spending, versus
the facts and current wealth values truly connected to each consumer (Cooper & Dynan, 2013).
40
Less than four years after the global recession, research was again showing positive
outlooks on consumer spending in the years to come given the recent increase in stock market
values (DeLisle, 2013). Low interest rates and rising home values were also cited as drivers of
these results (DeLisle, 2013). With over eighteen weeks of positive stock market trends, the
essay noted record index levels and increased corporate reserves with strong earnings growth
estimates as factors behind increased consumer attitudes related to spending (DeLisle, 2013).
The fact that companies have been getting more out of their existing assets was noted as one
source of positive corporate results in recent quarters. As this trend plateaus, more companies
will be relying more heavily on revenue growth generated through increased consumer spending
into the economy (DeLisle, 2013). With interest rates predicted to continue at low levels, more
investors will be drawn to the stock market, thus creating increased trends for the foreseeable
future. The continued appearance of stock market growth should continue to spur consumer
spending for some time to come (DeLisle, 2013). An essay published in 2010 had the same
focus but examined in detail the change in consumers’ valuation of the dollar among different
generations, and the impact of this valuation from consumers on consumer spending (Bruner,
2010). The essay gave a brief history of money, in addition to a brief history of the value of a
dollar to validate the opinions expressed in the essay. The essay described how previous
generations used the ability of the dollar to convert into a metal, and the value associated with
this conversion as their basis of the dollar’s worth. In contrast, the current generations view the
stock market and other macroeconomic factors as their guidance on the dollars worth (Bruner,
2010). Questions around how we should value the dollar, and what would happen to these values
if another crisis occurred were addressed in this essay (Bruner, 2010). The lack of understanding
when evaluating the true worth of the dollar was noted as a large factor in the last financial crisis
41
(Bruner, 2010). In previous generations, the value of the dollar was backed by silver or a
tangible item. Now, as the essay suggests, the value of the dollar is tied to the ability of the
population to produce goods and service, along with the ability of the United States government
to meet its financial obligations (Bruner, 2010). In conclusion, the essay notes our challenge
continues to revolve around our ability to see the value of the dollar for what it truly is,
considering all our challenges and shortcomings as a country and economy (Bruner, 2010).
While it is universally believed that economic health and consumer confidence levels go
hand in hand, one study evaluates an anomaly to this belief. Stock market performance is widely
believed to be an indicator of overall economic stability, but when looking at consumer
confidence intervals, this is not always the case (Ferrer, Salaber, & Zalewska, 2016). A recent
study evaluated the correlation between consumer confidence levels and stock market
fluctuations. While multiple research articles have been published around the short-term impact
of consumer confidence on stock market performance, the long-term implications have not been
clearly identified (Ferrer et al., 2016).
Post hoc data analysis was done for the time periods covering the last two recessions to
evaluate the strength of the stock market and consumer confidence relationship. While both
recessions were similar in size, they had different outcomes with varying lengths of impact on
the overall economy (Ferrer et al., 2016). To generate the results, the study used the consumer
confidence index in comparison to the stock market indexes published during the timeframes
studied. The research concluded that indirect impacts of stock market performance on the
consumer confidence levels were not significant to draw a conclusion and further research is
needed. Indirect impacts from stock market performance on the perceptions about future
personal finances were shown as strong (Ferrer et al., 2016). The final conclusions indicated that
42
consumers’ understanding of the basic stock market and its implications to the overall economy
may be more advanced than widely believed. This information may cause future forecasting
issues based on the variables having a larger impact than originally believed (Ferrer et al., 2016).
While unemployment and substantial drops in housing prices drove a large portion of the
great financial recession, huge stock market declines also had a substantial impact. The mean
net worth of households fell from five-hundred and ninety-five thousand in 2007 to four-hundred
and eighty-one thousand in 2009 (Kyoung & Hanna, 2016). While the stock market losses did
not impact a large portion of working Americans, their views and savings patterns where altered
for the foreseeable future. Investors have since placed retirement savings in less risky
investment vehicles, at a more consistent rate, decreasing their disposable income monthly
(Kyoung & Hanna, 2016).
The purpose of the previously mentioned study was to establish the impact of the stock
market decreases during the great financial crisis in relationship to the total wealth of working
households. Data from the survey of consumer finances published by the Federal Reserve on a
triennial basis was used in comparison to stock market trends for this study (Kyoung & Hanna,
2016). An equation was used to calculate the impact using equity holdings, total wealth, and the
percentage of changes in the Wilshire 5000 index. Human wealth, or the present value of an
individual’s future salaries, wages, self-employment earnings, anticipated pensions, and social
security benefits were also included in the calculation (Kyoung & Hanna, 2016). With only four
percent of household wealth residing in equity assets, the potential loss from the decrease in the
stock market was barely over one percent. Only five percent of households between the ages of
fifty-four and seventy had the potential to lose eight percent or more of their wealth (Kyoung &
Hanna, 2016). Younger households were at an even lower level of risk based on their percentage
43
of wealth invested in equities. While the top one percent and oldest members of the investment
community are at greatest risk when stock market indexes decline, over twelve percent of the
losses sustained during the latest crash could have been avoided by not selling all equity
investments near the height of the recession (Kyoung & Hanna, 2016).
Interest Rates
While interest rates and consumer spending have not been directly linked as they relate to
everyday purchases (Redmond, 2001), research has shown a direct relationship between interest
rates and large ticket purchases, including automobiles and homes (Barnes & Olivei, 2013). The
research used a survey asking questions regarding multiple economic environment issues
including unemployment, inflation, buying conditions, income, wealth, prices, and interest rates
(Barnes & Olivei, 2013). Consistent with other studies, the research found that income and
wealth are associated with consumer sentiment. In a unique angle, the study also related pricing
and interest rates as an explanatory factor to consumer sentiment (Barnes & Olivei, 2013). When
comparing the two metrics against actual consumer sentiment and spending, pricing and interest
rate trends followed actuals at a higher rate when compared to income and wealth levels over the
same period (Barnes & Olivei, 2013). The trickle-down effect of these large ticket items has also
been directly linked to overall consumer spending (McCarthy & Steindel, 2007a). So, as pricing
and interest rates for large ticket items are decreasing, consumer spending is rising for these
items, then trickling down into everyday items, thus generating overall stimulus to the economy
(McCarthy & Steindel, 2007a).
One study in 2011 examined the relationship between interest rates and spending from
the cyclical perspective and the planned perspective (Kandil & Mirzaie, 2011). The first theory
44
involved in the study separated expected and unexpected events in relationship to consumption
by consumers (Kandil & Mirzaie, 2011). Correlation coefficients were used to understand the
variations between the real growth rates of consumption versus the growth rate of disposable
income (Kandil & Mirzaie, 2011). To better understand the data, the private consumption totals
were segmented into total consumption, durable goods, non-durable goods, and services (Kandil
& Mirzaie, 2011). While decreases in interest rates did encourage future planned spending, it
was not a driver for increased spending on a day to day basis (Kandil & Mirzaie, 2011). When
interest rates lifted, or were predicted to increase, both cyclical and planned spending were
shown to decrease (Kandil & Mirzaie, 2011).
A study conducted in Japan, a country that has experienced many years of low-interest
rates, confirmed this relationship during the current timeframe but experienced lower levels of
future expected spending in similar interest rate conditions (Ichiue & Nishiguchi, 2015). Data
gathered in the opinion survey from the Bank of Japan was used for the study (Ichiue &
Nishiguchi, 2015). Current daily spending was shown to increase during this extended period
with low-interest rates but expected future spending was on the decline due to inflationary
worries (Ichiue & Nishiguchi, 2015) . As expected, pricing changes exist due to future interest
rate predictions, projected and actual spending will follow. When pricing is expected to increase
even slightly, sales are expected to decrease (Ichiue & Nishiguchi, 2015). The inverse is also true
with slight downturns in pricing driving expected spending increases. During the timeframe
studied, pricing increases driven by higher interest rates were always followed by decreased
spending in the following months (Ichiue & Nishiguchi, 2015). Opinions of consumers during
the same timeframe also preceded increases in compensation of employees, and thus predicted
the increases of real consumption (Ichiue & Nishiguchi, 2015). Rising interest rates also created
45
lower future expectations causing an increase in the likelihood of decreased spending on larger
ticket items, driving the overall spending total down across the board if interest rates do not
increase (Ichiue & Nishiguchi, 2015). The results in Japan differed from the results in the United
States with inflation increases actual boosting the economy as opposed to America where
inflation has been known to decrease spending (Ichiue & Nishiguchi, 2015).
Another recent study evaluated the combined impact of oil pricing on interest rates and
the resulting availability of credit along with the impact these factors had on consumer spending.
The hypothesis of the study revolved around consumers spending more on credit during times of
high oil pricing due to the increased availability of credit (Arora, 2016). The theory went on to
explain that when gasoline purchases were made on credit, disposable income was spent on other
items in the economy, thus generating overall economic growth.
The study also contended that the trends for consumer credit and gasoline spending in the
United States moved at a similar rate. Availability of credit and the cost associated was
examined in the remainder of the study, along with an analysis of the importance of oil pricing in
comparison to the overall economy (Arora, 2016). Throughout the literature review, the research
found that when consumers had access to credit, there were smaller adjustments in regards to
spending when oil pricing shifted. As credit balances grew over time, this trend may have just
shifted the timing of a recession rather than avoiding one (Arora, 2016).
Outstanding credit and credit transactions have grown over the years in the United States,
with the cost associated with this credit declining at a substantial rate. To analyze the correlation
between changes in credit and gasoline expenditures, the research used simple scatterplots,
different types of correlation coefficients, and estimating tail coefficients to develop the research
46
(Arora, 2016). The raw data used was collected from the Federal Reserve’s month G19 releases
and the Bank for International Settlements reports around total credit. The correlation and
scatterplot analysis both showed a growing association between growth in various measures of
credit and gasoline spending during the time evaluated (Arora, 2016).
While the research concluded that the relationship between credit adjustments and
gasoline spending existed, it did not conclude that they were directly responsible for each other.
As total spending in the United States has shifted from mainly cash to an economy where many
things are bought on credit, the direct impact of economic shifts may not be fully felt
immediately (Arora, 2016). While increases in gasoline prices may have impacted customer
spending, the true impact may be delayed depending on the availability of credit in the economy
(Arora, 2016).
When developing nations begin to grow, a crucial determinant of their long term success
will revolve around their interest rate and monetary policies. When long-term development
begins to emerge in these developing nations, higher interest rates begin to occur (Velickovic &
Velickovic, 2016). As this increases the cost of capital in the developing county, investment
demands may decrease, hindering future growth if not done correctly (Velickovic & Velickovic,
2016).
While developed countries have used higher interest rates in a successful manner to
properly manage demand, this has not been done in developing nations (Velickovic &
Velickovic, 2016). These developed countries can change the discount rate if a sector of the
economy is not responding to the interest rate policy in place (Velickovic & Velickovic, 2016).
This ability to react and recover is not available for developing countries encountering the same
issues. Current research analyzes if a high interest rate environment in a developing country
47
would provide an environment for the country to naturally develop in a manner that would
generate long-term growth (Velickovic & Velickovic, 2016).
Short term interest rate hikes have influenced the growth of real cost of capital in the
past, therefore having had an impact on investment demand. This increase or decrease related to
investment demand also had an ongoing impact on interest rates (Velickovic & Velickovic,
2016). The question evaluated by the study revolved around analyzing the relationship of these
two factors.
The IS-LM model was used in this study and evaluated the simultaneous equilibrium in
the goods market and the money market. The model focused heavily on interest rate,
investments, and exchange rates to generate its calculations (Velickovic & Velickovic, 2016).
The model did have some weaknesses when evaluating developing countries, including its
disregard of persistent inflation and its impact on investment goods demand (Velickovic &
Velickovic, 2016). The model concluded that investments were the function of the gap between
the desired size of capital and the income growth and expected inflation (Velickovic &
Velickovic, 2016).
In conclusion, the model was more successful when used at higher development levels
with market-regulated economies with low inflation rates. In these situations, increases in real
interest rate levels had a greater influence on the decline of investments (Velickovic &
Velickovic, 2016). The model also found that in the long run, interest rate decreases had the
inverse effect on investment growth and took the country into an inflationary period (Velickovic
& Velickovic, 2016).
48
Developing countries in the end have failed to create a full functioning financial
marketing system ready to make the developmental processes able to succeed. This lack of
proper planning also generated difficulties related to the regulation of the developing market
(Velickovic & Velickovic, 2016). The issues that accompanied many of these developing
countries included chronic underdevelopment and a consistent demand for increased capital
(Velickovic & Velickovic, 2016).
High interest rate levels have been successful in developed nations due to the expectation
of inflation, and the nation’s desire to close the gap between current and desired levels of capital
in the system. These nations can also adjust the discount rate as needed, which is crucial for
success (Velickovic & Velickovic, 2016). The research concluded that these developed countries
must control private and public consumption during recessionary times to be successful in the
long-run (Velickovic & Velickovic, 2016).
In contradiction to other studies, a recent study noted that during the most recent
economic recovery in the last three years, lower interest rates have driven only modest increases
in spending on durable goods (Van Zandweghe & Braxton, 2013). The items included in the
durable segment consisted of residential investments, vehicles, recreational goods, and
household goods (Van Zandweghe & Braxton, 2013). This study compared the current recovery
versus the previous three economic recoveries for the United States economy. Real GDP,
consumption expenditures, residential investment, vehicles purchases, and household
expenditures were all evaluated (Ichiue & Nishiguchi, 2015). While spending, recovery lagged
other recoveries, interest rate increases placed ahead of similar recoveries, providing validity to
the theory connecting increased interest rates and decreased future spending amounts (Ichiue &
Nishiguchi, 2015). Another recent study voiced the notion of a decreased impact generated by
49
interest rate changes in the United States economy (Willis & Cao2, 2015). The recent study
conducted for the Economic Review periodical indicated that employment and industrial
decisions have been less impacted by interest rates in recent years (Willis & Cao2, 2015). The
study noted changes from monetary policy makers, innovations in financial markets including
changes in governmental regulations, and changes across and within industries as drivers of this
shift (Willis & Cao2, 2015). Long term interest rate changes are now having a great impact on
industries versus short-term shifts that created reactions in the past years (Willis & Cao2, 2015).
The model created used four variables including the federal funds rate, total nonfarm payroll
employment numbers, the Chicago Fed National Activity Index, and the price index. In
conclusion, the research indicated that industries have realized this shift, and are adjusting
accordingly (Willis & Cao2, 2015). This shift by industries has generated a decline in
dependence upon the interest rate, as in not the result of monetary policy changes (Willis &
Cao2, 2015).
In previous economic recovery time periods, lower interest rates generated a larger
increase in spending compared to the recent timeframe of recovery (Van Zandweghe & Braxton,
2013). As both the employment levels and industrial investments have stabilized in relationship
to interest rates, so have consumer spending habits during this recent period of recovery (Willis
& Cao2, 2015). As consumers see a more stable stream of income, and expectation of income,
spending has leveled out in relationship to interest rate changes, reacting at lower levels when
shifts occur (Willis & Cao2, 2015).
50
Employment Rates
Research has shown that as employment rates decline, consumer spending will
soon follow (Howard & Shipps, 2013). A lack of jobs in an economy has been shown to lead to
foreclosures, homelessness, and bankruptcies (Howard & Shipps, 2013). To understand the
impacts of employment rates on the economy, the study evaluated the aspects related to these
numbers for their findings. They identified four types of unemployment including frictional, or
unemployment from individuals changing jobs, seasonal unemployment due outdoor and other
seasonal factors, structural employment driven by a lack of skills needed to perform the jobs that
are available, and cyclical unemployment caused by changes in the business cycle during
recessions or expansionary periods (Howard & Shipps, 2013). Government workers, educators,
and employees of financial firms have lower levels of unemployment during recessionary
timeframes (Howard & Shipps, 2013). Education levels also played a major role with a
bachelor’s degree increasing the chance of employment by almost double when compared to
employees with only a high school diploma (Howard & Shipps, 2013). Since the great financial
recession of 2009, unemployment rates have declined, but not at the same levels as in past
recovery periods. Many factors were identified as factors keeping unemployment numbers
above desired levels, including large companies still struggling since the recession, job cuts by
large firms to reduce costs, low rates of job creation, firms holding cash versus expanding,
companies expanding without expanding their workforce, fundamental changes in labor markets,
and extension of unemployment benefits (Howard & Shipps, 2013). While these factors may not
have caused high unemployment levels, they have impacted the sustained high levels that still
exist(Howard & Shipps, 2013). Businesses shifting abroad, declining business formation, an
accelerated pace of automation, the unwillingness of companies to hire unemployed individuals,
51
Congressional issues, spillovers from financial issues in Europe, and cutbacks at the federal,
state, and local levels have also been identified as drivers for prolonged unemployment levels
(Howard & Shipps, 2013). These situations obviously decrease the amount of spending from
the individuals involved, but also affect the spending and investment capabilities of the landlords
and banks holding the loans for the homeowners (Howard & Shipps, 2013).
To stabilize employment rates, research has been conducted on methodologies that could
be used in the pursuit of this goal. One recent article in the Eastern Economic Journal displayed
research that introduced a model reducing firm’s mark up expectations, which in turn would
increase consumer spending (Gatti, 2009). This model concluded that by decreasing the mark up
expectations from industries, lower prices on goods would drive consumer spending and in turn
drive employment rates up (Gatti, 2009). The study’s findings determined that as unemployment
rates decline, consumer spending would increase enough to make up for taxation shortfalls, in
addition to driving industrial expansion that would sustain the economy in the long run (Gatti,
2009).
As noted in other studies (Heim, 2010a), consumers have been shown to sustain a
consistent level of spending, even during times of declining employment rate (Florea, Moise,
2014). A recent study showcased this fact by identifying the lack of consumers’ motivation to
change consumption habits during times of unemployment (Florea, Moise, 2014). The use of
correlation coefficients was used to evaluate the change of employment rate and changes in
unemployment benefits in comparison to the monthly expenditures per person. While a change in
unemployment benefits obviously spurred a change in expenditures, the same did not exist
between employment status changes and expenditures (Florea, Moise, 2014). These consumers
52
used savings or acquired help from friends and family to sustain their current spending habits
during employment difficulties (Florea, Moise, 2014). When unemployment benefits were
expanded, so were spending levels above their normal levels, indicating that both methods of
obtaining funds were used to generate these spending levels (Florea, Moise, 2014).
To determine how sustained levels of spending have occurred during low levels of
employment, research was conducted on consumers going through economic hardships and
unemployment (Baek & DeVaney, 2010). This study determined that consumers either used their
own savings or credit to sustain spending habits and norms during times of low or no
employment (Baek & DeVaney, 2010). To generate their results, the study used data gathered
from the survey of consumer finances. They used these results to generate a conceptual model
based on the risk management theory and the permanent income hypothesis (Baek & DeVaney,
2010). Over half of the respondents used borrowed money or credit cards to meet their spending
needs. Another third of the respondents used funds from their savings (Baek & DeVaney, 2010).
The use of credit by individuals without savings further widened the gap between classes as debt
levels increased on the lower levels of the economy (Cynamon & Fazzari, 2013).
While economies are obviously dependent on employment to drive prosperity, it is worth
determining the impact that the employment level in a country drives the overall economy. A
recent study posed this question by evaluating and comparing this impact in the United States,
the United Kingdom, and Japan (Caporale, Gil-alana, & Lovacha, 2016). Previous research has
indicated that unemployment rates will follow shifts in a country’s business cycle, and the level
of correlation between these two factors will determine a country’s dependence on employment
levels. The two main theoretical approaches to employment levels that were discussed included
53
the natural rate theory and hysteresis models developed in Europe in the late 1980’s (Caporale et
al., 2016). A natural level determined by economic fundamentals of an economy is the basis of
the natural rate theory with the hysteria model being driven by shocks to an economic system
that have long lasting effects (Caporale et al., 2016). Multivariate regression analysis was done
using data from the St. Louis Federal Reserve Bank database for this study. The results of the
study indicated that both the United Kingdom and Japan had a high level of correlation between
unemployment rates and the overall economies’ performances. The data related to the United
States indicated that the economy was not dependent upon the unemployment levels at a
significant value (Caporale et al., 2016).
Further research has indicated that past research saw a negative impact on the United
States economy when social services were extended to reduce unemployment levels (Li & Lin,
2016). This current research evaluates the phenomenon of stagflation, or an extended period of
high inflation and high unemployment numbers, and its’ impact on an economy. To perform this
analysis, government released data related to government social benefits, gross domestic product
changes, unemployment rates, and the consumer price index were evaluated (Li & Lin, 2016).
This data was gathered from the United States Bureau of Labor Statistics Department. While
this stagflation was evident in the late 1970’s and early 1980’s, it was avoided during the most
recent recession due to a lack of inflation while unemployment levels peaked (Li & Lin, 2016).
An autoregressive distrusted lag bounds testing approach to cointegration was used to evaluate
the data. The research concluded that social benefit expenditures created a drag on economic
growth (Li & Lin, 2016). It also concluded that increased monetary supply generated by these
social benefit expansions would lead to inflation, and would encourage more individuals to
remain in the unemployed category (Li & Lin, 2016).
54
The levels at which hotel destinations are impacted by economic shifts in comparison to
economic shifts has been widely discussed. One recent study evaluated the impact of
employment levels on the hotel industry in Spain in comparison to the residential developments
during recent economic shifts (Perles-Ribes, Ramón-Rodríguez, Sevilla-Jiménez, & Moreno-
Izquierdo, 2016). The National Institute of Statistics in Spain was used to gather the data needed
to evaluate unemployment rates and economic activities in the locations analyzed in the study.
Regression analysis was done on the two sets of data to generate the results. Hotel locations
were found to be more resilient during times of economic shifts when compared to residential
developments (Perles-Ribes et al., 2016). Large variances in the real estate market was shown as
a major driver to this difference. The difference of impact remained consistent when looking at
retail activities, restaurants, and bars throughout hotel and residential locations (Perles-Ribes et
al., 2016). All activities located in proximity of the hotel sector performed better during these
timeframes. The amount of holiday driven engagement at hotel locations was a driving factor for
its’ performance when compared to residential locations (Perles-Ribes et al., 2016).
Employment rates have been used to explain many economic factors. One recent study
noted the limited amount of research evaluating unemployment levels in comparison to
consumer debt levels (Shaffer & Zuniga, 2016). The fact that a decline in debt levels followed
the latest financial crisis with lower interest rates generated the question at hand. With
unemployment levels at all-time highs, the research examined the relationship between the two
to see if a correlation exists (Shaffer & Zuniga, 2016). The hypothesis of the study stated that
there is an inverse relationship between unemployment rates and consumer debt. The thinking
behind this theory was that households may reduce debt on their own due to expectations of
future wealth and job opportunities (Shaffer & Zuniga, 2016). The belief that lenders may
55
reduce their levels of lending based on this same expectation is another foundation of this theory.
Regression analysis was performed on data including the total consumer debt, consumer
revolving debt, consumer mortgage debt, the ratio of total consumer debt to disposable personal
income, and the debt service ratio for total consumer debt. The Federal Reserve Economic
Database was used to gather the data (Shaffer & Zuniga, 2016).
The results indicated that unemployment rates had a significant impact on levels of debt
with the exclusion of mortgage debt. This was basically driven by consumers’ inability to reduce
mortgage debt as quickly or easily as they could adjust other forms of consumer debt (Shaffer &
Zuniga, 2016). The conclusion of the study confirmed that employment rates and interest rates
should be evaluated as a large component of the equation when evaluating consumer debt levels
(Shaffer & Zuniga, 2016).
Consumer Credit Index
As a direct input into the amount of income that is available for consumers to spend, the
consumer credit index, or the availability of credit or loans within the United States economy is a
driving factor within research around consumer spending (Bearden & Haws, 2012). After
researching self-control and spending decisions, a current study found that credit limits were a
deciding factor for consumers when purchasing a home, and sometimes a factor for everyday
items (Bearden & Haws, 2012). The study used multiple surveys to conclude that self-control
mechanisms failed, even with the results driving negative personal and social implications for the
consumer (Baek & DeVaney, 2010). Knowing this lack of self-control is evident, the research
concluded that many consumers use credit card companies and mortgage lenders to set the
spending limits that to which they adhere (Baek & DeVaney, 2010). Even so, consumers have
56
still been known to spend up to the limit given due to this lack of self-control (Baek & DeVaney,
2010). As more controls, have been implemented to control debt levels, overall spending levels
have been negatively impacted. Prior to the crisis of 2008, large amounts of home equity loans
were made available, flowing income into consumers’ pockets that were used in the years
leading up to the crisis on an everyday basis (McCarthy & Steindel, 2007b).
After the crash of 2008, debt levels increased as home prices decreased (Dynan, 2012) .
A study concluded that households with these increased levels of debt have lower levels of
spending versus households with smaller amounts of debt (Dynan, 2012). While increased levels
of credit spurred short-term spending, in the long run, consumer spending was impeded by
higher levels of debt (Dynan, 2012). As these consumers with large amounts of mortgage debt
increased their wealth, the existing large amounts of debt generated by available funds in
previous years have restricted the amount of spending going forward (Dynan, 2012). A
longstanding panel survey called the PSID was used to gather the data used in the study. As the
ratio of debt associated with mortgage loans increased leading up to the great financial crisis, the
opposite trend existed regarding the percent of disposable personal income that existed for each
household (Dynan, 2012). These consumers have also been noted to have issues even paying
their mortgage payments due to the debt burden this generates (Dynan, 2012). While this issue
should be decreasing over time due to new mortgage lending practices, the group of consumers
in this situation is expected to grow by seven percent in the short term (Dynan, 2012). As more
households fall into this category, increased pressure will be put on the economy due to
decreased spending from this group (Dynan, 2012).
57
Summary
This brief review of the literature has examined consumer spending, and the areas of
macroeconomics that research has shown to impact consumer spending. Some brief examples
using the theory of planned behavior in relation to the study were also included. This topic will
be expanded on with the full literature review contained in the study. The literature reviews also
discussed the regional theme park industry, and its’ need for consumer spending at parks to
generate positive results. The combination of these selections of literature and research should
lead to the proposed research.
58
Chapter 3: Research Method
Current research has evaluated the effects of macroeconomic downturns on consumer
spending in a variety of industries when metrics like consumer confidence, interest rates,
unemployment rates, stock market values, and consumer credit index have shifted (Ma et al.,
2011). However, there were industries, such as regional theme parks, that are separate from other
retail, travel and tourism destinations because of their unique variables (Salamat & Banik,
2013a). The specific problem was that consumer discretionary spending was impacted by
macroeconomic trends, and although there was research on the impacts of macroeconomic trends
in relationship to various industries, there was no current information on how changes in
macroeconomic trends impacted regional theme park attendance and overall revenue results. The
purpose of this non-experimental quantitative method of inquiry, correlation design utilizing ex
post facto quantitative research was to understand how the consumer confidence index, stock
market values, interest rates, unemployment rates, and the consumer credit index impacted
regional theme park attendance and revenue performance.
In a recent journal article titled Suitability of three different tools for the assessment of
methodological quality in ex post facto studies, researchers created a tool to evaluate the
suitability of ex post facto research when evaluating historical data (Jarde, Losilla, & Vives,
2012). Their findings concluded that this method of research produced great reliability on both
global and local scores (Jarde et al., 2012). The reliability of a proper ex post facto study was a
major factor in the research method design for the proposed study. To complete a proper ex post
facto study, multiple items must have been in place. This included comparableness of the
participants for all important characteristics excluding the actual numbers that are being
59
evaluated, reliable instruments to collect the data, complete data that will not prohibit proper
statistical analysis, and the funding needed to complete the study without a conflict of interest.
The trustworthiness of the results at both the national and regional level were also a key
advantage of using the ex post facto research design for this study (Jarde et al., 2012). The
proposed study was evaluated against this set of criteria and met all requirements. With the data
gathered from verified public sources, no issues related to data quality or completeness were
present. The use of this free public data also eliminated funding issues. The data being used for
the study was verified public information, so the completeness and validity of the data was
sound. All five of the independent variables included in the proposed study were merged
together to determine if there was any correlation with the dependent variable and if there was
covariance between the means of these variables and the results they produce when compared to
the dependent variables. The problem statement generated the following nine research questions
with respective null and alternative hypotheses:
Q1. To what extent, if any, is there a relationship between merging all five predictive
variables (consumer confidence index, stock market values, interest rates, unemployment rates,
and the consumer credit index) together and attendance at Cedar Fair theme parks?
Q2. To what extent, if any, is there a relationship between merging all five predictive
variables (consumer confidence index, stock market values, interest rates, unemployment rates,
and the consumer credit index) together and revenue performance at Cedar Fair theme parks?
Q3. To what extent, if any, is there a relationship between merging all five predictive
variables (consumer confidence index, stock market values, interest rates, unemployment rates,
and the consumer credit index) together and attendance at Six Flags theme parks?
60
Q4. To what extent, if any, is there a relationship between merging all five predictive
variables (consumer confidence index, stock market values, interest rates, unemployment rates,
and the consumer credit index) together and revenue performance at Six Flags theme parks?
Q5. To what extent, if any, is there a covariance within the predictive variables consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index?
H10. There is no relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and attendance at Cedar Fair theme parks at a statistically significant level.
H1a. There is a relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and attendance at Cedar Fair theme parks at a statistically significant level.
H20. There is no relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and attendance at Six Flags theme parks at a statistically significant level.
H2a. There is a relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and attendance at Six Flags theme parks at a statistically significant level.
H30. There is no relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and revenue performance at Cedar Fair theme parks at a statistically
significant level.
61
H3a. There is a relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and revenue performance at Cedar Fair theme parks at a statistically
significant level.
H40. There is no relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and revenue performance at Six Flags theme parks at a statistically
significant level.
H4a. There is a relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and revenue performance at Six Flags theme parks at a statistically
significant level.
H50. There is no significant covariance within at least one of the predictive variables
consumer confidence index, stock market values, interest rates, unemployment rates, and the
consumer credit index.
H5a. There is a significant covariance within at least one of the predictive variables
consumer confidence index, stock market values, interest rates, unemployment rates, and the
consumer credit index.
Research Methods and Designs
A non-experimental quantitative method of inquiry, utilizing ex post facto quantitative
research, was used to understand how the consumer confidence index, stock market values,
interest rates, unemployment rates, and the consumer credit index impact regional theme park
attendance and revenue performance. The data gathered used publicly distributed records
62
consisting of annual totals for both sets of dependent variables. Both sets of confirmed data
allowed the researcher to use correlation analysis to determine the relationship between both sets
of variables. Multiple research articles produced by Carter (Carter, 2014), along with the
previously mentioned study from Jarde (Jarde et al., 2012) in 2012 has validated the use of
multiple regression analysis to evaluate multiple variables against financial performance metrics
to understand correlations or a lack thereof between the variables (Carter, 2015). The use of this
method within other financial studies also gave validity to the analysis method used in this study
(Carter, 2014). To understand how the consumer confidence index, stock market values, interest
rates, unemployment rates, and the consumer credit index impacted regional theme park
attendance and revenue performance, the following study used the interval values of regional
theme park attendance and revenue performance as the dependent variables. Cedar Fair
Entertainment and Six Flags annual reports served as reliable and accurate sources for the data
related to both dependent variables. The independent variables that consisted of the consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index were also interval values ranging in values depending upon the specific independent
variable. The method, design, and methodology used in the study were appropriate for
completing the purpose of study and answering all research questions.
Population
The exact population for this study was Six Flags and Cedar Fair Entertainment. The use
of each corporation’s annual reports over the timeframe evaluated achieved the purpose of
obtaining their attendance and revenue performance over the same timeframe. The target
population included the two largest entities in the United States regional theme park industry. In
North America alone, Six Flags Entertainment Corporation operates twenty locations in two
63
countries and multiple states (Six Flags Entertainment Corp. (2015). 2015 Annual Report. Grand
Prairie, TX: Author). Not to be outdone, Cedar Fair Entertainment Corporation operates over
thirteen locations in two countries and multiple states (Cedar Fair Entertainment Corp. (2015).
2015 Annual Report. Sandusky, OH: Author). The closest competitor to these two companies
operated six locations in five states, with other industry participants falling below these totals
(SeaWorld Entertainment Incorporated. (2015).2015 Annual Report. Orlando, FL: Author).
Sample
A sample size of two corporations consisting of thirty-three regional theme parks was
selected as they represented approximately sixty-six percent of the regional theme park industry
across the United States. Due to the market share of the participants in the study, no reduction in
data collection was needed. Participant limitation was also not required due to the depth of the
sample size provided.
Materials/Instruments
The data was gathered using publicly distributed records consisting of annual totals for
both sets of dependent variables. Having both sets of confirmed data allowed the researcher to
use correlation analysis to determine the relationship between both sets of variables. Multiple
research articles produced by Carter (Carter, 2014), along with the previously mentioned study
from Jarde (Jarde et al., 2012) in 2012 validated the use of multiple regression analysis to
evaluate multiple variables against financial performance metrics to understand correlations or a
lack thereof between the variables (Carter, 2015). The use of this method within other financial
studies also gave validity to the analysis method used in this study (Carter, 2014).
64
Operational Definition of Variables
To understand how the consumer confidence index, stock market values, interest rates,
unemployment rates, and the consumer credit index impact regional theme park attendance and
revenue performance, the following study used the interval values of regional theme park
attendance and revenue performance as the dependent variables. Cedar Fair Entertainment and
Six Flags annual reports served as reliable and accurate sources for the data related to both
dependent variables. Likewise, the independent variables that consisted of the consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index were also interval values. Each of the independent variables was published
nationally on at least an annual basis generating reliable and accurate data for the study.
Revenue Totals
The first dependent variable of the study was the revenue totals for Six Flags and Cedar
Fair Entertainment on an annual basis. The variable was an interval value ranging between $976
million and $1.175 billion for each company published in their annual report to the stock market.
The raw data for the variable were interval values gathered from the annually archived report for
each company for each year evaluated.
Attendance Totals
The second dependent variable for the study was the number of people that attended Six
Flags and Cedar Fair Entertainment on an annual basis. The variable was an interval value
between 22.1 million and 26.1 million people for each company published in their annual report
to the stock market. The raw data for the variable was gathered from the achieved report for each
year evaluated.
65
Consumer Confidence Index
Consumer confidence index values were the first independent variables for the study and
were published monthly. The variable was an interval value between 0 and 160 percent that was
published monthly online by an independent economic research firm called The Conference
Board. The number indicated the amount of confidence that consumers have in the economy, so
the higher the number, the greater the confidence of consumers. The value of the index
typically ranges around 60-140 with a value of 100 being an average middle point of consumer
confidence. The raw data for the variable was gathered from the achieved report to understand
the average value for each year evaluated.
Stock Market Values
The second independent variable for the study was the annually published stock market
value. The variable was a positive or negative interval value between 0 and 10,000 based on the
change in the stock market value over the course of a year. If the total value of the stock market
went up over the course of the year, the number was positive, with a decrease in the value of the
stock market generating a negative number. The raw data for the variable was gathered from the
archived report that was published annually by the New York Stock Exchange for each year
evaluated.
Consumer Interest Rates
The third independent variable for the study was published consumer interest rates. The
variable was a positive or negative interval value between 0 and 10 percent, based on the annual
change in consumer interest rates for each given year. For example, if interest rates went up one
percent over the course of a year, the change in interest rates was 1%. If interest rates fell one
66
percent over the year, the change in interest rates was -1%. The raw data for the variable was
gathered from achieved reports for each year evaluated.
Unemployment Rates
The fourth independent variable for the study was the published annual unemployment
rate. The variable was an interval value between 4 and 12 percent. The number was calculated
by dividing the number of unemployed workers by the number of total workers in an economy.
Full employment in an economy generated an unemployment rate of 0 percent. The raw data for
the variable was gathered from achieved reports published by the United States Department of
Labor on an annual basis.
Consumer Credit Index
The fifth independent variable for the study was the published annual consumer credit
index. The variable was generated by analysis evaluating loan repayment records, the use of
revolving credit, estimated household cash flow, and the relative cost of servicing outstanding
debt. The value of the index was an interval value between 40 and 65, with the higher numbers
indicating a healthier consumer credit environment within an economy. The raw data for the
variable was gathered from archived reports published by TransUnion, an independent economic
research firm, on an annual basis for each year evaluated.
Data Collection, Processing, and Analysis
The method of research was selected after ensuring that both the participants of the study
and the variables of the study fit within the proper framework of a correct ex post facto research
model (Jarde et al., 2012). Both companies were first found to be comparable on their main
components (Jarde et al., 2012) eliminating bias between the two corporations. To answer the
67
extent of which the consumer confidence index, stock market values, interest rates,
unemployment rates, and the consumer credit index impacted both attendance and revenue
performance at regional theme parks, this study evaluated Six Flags and Cedar Fair
Entertainment. These two corporations were selected as the study sample due to the percentage
of market share owned by the two companies in the regional amusement park industry.
Determining the relationship between the variables for these two corporations allowed the study
to determine if the independent variables, the consumer confidence index, stock market values,
interest rates, unemployment rates, and the consumer credit index impacted the dependent
variables of attendance and revenue performance at regional theme parks as a whole. This
knowledge addressed the research problem, research purpose, and the research questions related
to the study.
Multiple regression analysis was used to conduct statistical analysis on the dependent
variables, which were regional theme park attendance and total revenue performance from 2007-
2012, for both Cedar Fair Entertainment and Six Flags separately. Using quantitative regression
analysis allowed the examination of the relationship between the participants and each variable,
generating predictive factors and forecasting tools (Carter, 2014). This type of statistical analysis
also measured the level of influence driven by the independent variables on the dependent
variables (Carter, 2014). Regression analysis provided results that analyzed the factors
separately, generated predictor values when the independent variables changed, measured the
variability between the factors, and generated the credibility of the hypothesis or null hypothesis
depending upon the results (Carter, 2014). The research was conducted on an annual basis,
specifically analyzing the correlations or lack thereof between regional theme park attendance
and revenue totals compared to macroeconomic trends for each year evaluated. Annual financial
68
reports from both Six Flags and Cedar Fair Entertainment were used to gather the needed
attendance and revenue performance metrics as the dependent variables for each year evaluated.
The annual financial reports for both companies were publicly available online. In a similar
manner, data collection was performed on publicly available websites to assemble the
macroeconomic reports related to each independent variable during the timeframes evaluated.
The processing and analysis of the data followed with a summary of the research concluding this
section.
Assumptions
It was assumed that both participants absorbed impacts related to weather at each park
due to the national scope of their locations. It was also assumed that local economic impacts
were mediated across the scope of each corporation’s parks due to the distance between each
location. It was lastly assumed that results reported by each corporation on a quarterly basis were
truthful and reliable as a source for this study.
Limitations
Sample selection of the participants from Six Flags and Cedar Fair were limited to these
participants. With the limited scale and limited public information related to the other businesses
in the regional theme park industry, these two participants were selected. The lack of all
participants within the industry does pose a certain degree of unreliability to the study. Data
related to attendance at both subject’s parks was limited to annual data. This limitation of data
restricted the regression models ability to provide significance. Data related to each
corporations’ revenue performance was available on a quarterly basis, thus eliminating this
restriction for analysis pertaining to revenue performance.
69
Ethical Assurances
The validity and reliability of both the data and the methods to collect the data have also
been deemed appropriate and trustworthy (Jarde et al., 2012). It was also concluded that no
conflicts of interest existed that influenced the study inappropriately. The findings were used to
understand the impact on regional theme park attendance and revenue performance given
specific estimates of future macroeconomic trends related to the consumer confidence index,
stock market values, interest rates, unemployment rates, and the consumer credit index.
Summary
Although there was research on the impacts of macroeconomic trends for a variety of
industries, there was no information on how changes in macroeconomic trends impacted regional
theme park attendance and overall revenue results. Research into the relationship between the
dependent and independent variables, and the correlation or lack thereof between the
independent variables and dependent variables provided the information needed for regional
theme parks to plan for and react properly to changing macroeconomic conditions. This study
added to the body of literature on regional theme park attendance and revenue performance with
insightful information for the industry regarding how they can plan and react to changes in
macroeconomic trends. The research method was designed to meet the study purpose of (1)
examining the relationship between changes in macroeconomic trends and regional theme park
attendance, and (2) examining the relationship between changes in macroeconomic trends and
regional theme park revenue performance.
70
Chapter 4: Findings
The purpose of this quantitative ex post facto study was to understand how
macroeconomic indicators including consumer confidence index, stock market values, interest
rates, unemployment rates, and the consumer credit index impacted regional theme park
attendance and revenue during times of different macroeconomic conditions. The independent
variables for this study were publicly reported consumer confidence indexes, interest rates,
unemployment rates, stock market trends, and the consumer credit indexes during the timeframes
evaluated. The dependent variables were the attendance and revenue performance for all parks
at both Six Flags and Cedar Fair Entertainment. The research was conducted using annual data
spanning 2007-2012, specifically analyzing the correlation of these metrics to macroeconomic
trends for each year evaluated. Annual financial reports from both Six Flags and Cedar Fair
Entertainment were used to gather the needed attendance and revenue performance metrics as the
dependent variables for each year evaluated. Multiple regression analysis was used to conduct
statistical analysis on regional theme park attendance and total revenue performance during times
of macroeconomic shifts on an annual basis. This data gathered from 2007-2012 was evaluated
on a year by year basis or quarterly basis when available.
To understand how the consumer confidence index, stock market values, interest rates,
unemployment rates, and the consumer credit index impact regional theme park attendance and
revenue performance, the following study used the interval values of regional theme park
attendance and revenue performance as the dependent variables. Cedar Fair Entertainment and
Six Flags annual reports served as reliable and accurate sources for the data related to both
dependent variables. Likewise, the independent variables that consist of the consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
71
credit index were also interval values ranging in values depending upon the specific independent
variable.
Results
The quarterly and annual financial reports for both Cedar Fair and Six Flags were
analyzed versus macroeconomic indicators using the timeframe of 2007 through 2012.
Research Question 1 results. The following is a restatement of research question Q1
and the associated null and alternative hypothesis.
Q1. To what extent, if any, is there a relationship between merging all five predictive
variables (consumer confidence index, stock market values, interest rates, unemployment rates,
and the consumer credit index) together and attendance at Cedar Fair theme parks?
H10. There is no relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and attendance at Cedar Fair theme parks at a statistically significant level.
H1a. There is a relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and attendance at Cedar Fair theme parks at a statistically significant level.
The relationship between the five predictive variables (consumer confidence index, stock
market values, interest rates, unemployment rates, and the consumer credit index) and attendance
at Cedar Fair theme parks was evaluated using a backwards regression model. The linear
regression summary reflected no correlation between the variables, R=1.0 (see Table 1).
72
Table 1
Cedar Fair Attendance Regression Results
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 1.000a 1.000 . .
a. Predictors: (Constant), CCI B/W%, Unemployment B/W%, Interest Rate B/W%, Debt % of GDP B/W%, Stock
B/W%
The ANOVA value was also displayed at zero, indicating that the R Square value did not
significantly predict the outcome variable of attendance at Cedar Fair parks (see Table 2). The
insignificance of the R Square value along with the lack of significance from the ANOVA model
failed to reject the null hypothesis. Published data pertaining to attendance at Cedar Fair parks
was limited to annual data, thus limiting the ability of the model to generate significant
correlations. There was no support for the alternate hypothesis with the null hypothesis not
being rejected. No relationship exists between all five predictive variables and attendance at
Cedar Fair parks.
Table 2
Cedar Fair Attendance ANOVA Results
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression .027 5 .005 . .b
Residual .000 0 .
Total .027 5
a. Dependent Variable: Attn B/W%
b. Predictors: (Constant), CCI B/W%, Unemployment B/W%, Interest Rate B/W%, Debt % of GDP B/W%,
Stock B/W%
73
Research Question 2 results. The following is a restatement of research question Q2
and the associated null and alternative hypothesis.
Q2. To what extent, if any, is there a relationship between merging all five predictive
variables (consumer confidence index, stock market values, interest rates, unemployment rates,
and the consumer credit index) together and revenue performance at Cedar Fair theme parks?
H20. There is no relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and attendance at Six Flags theme parks at a statistically significant level.
H2a. There is a relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and attendance at Six Flags theme parks at a statistically significant level.
The relationship between the five predictive variables (consumer confidence index, stock
market values, interest rates, unemployment rates, and the consumer credit index) and revenue at
Cedar Fair theme parks was evaluated using a backwards regression model. The linear
regression summary reflected a correlation between the independent variables Debt as a % of
GDP and Interest Rates in relation to revenue performance at Cedar Fair parks, R Square=.263
(see Table 3). The other four models produced by the backwards regression model either proved
to be insignificant during the ANOVA analysis, or provided a lower R Squared total (see Table 3
and Table 4).
74
Table 3
Cedar Fair Revenue Regression Results
Model SummaryModel R R Square Adjusted R Square Std. Error of the Estimate
1 .589a .347 .165 .205760802000000
2 .581b .337 .198 .201691234000000
3 .554c .306 .202 .201133902000000
4 .513d .263 .193 .202277656000000
5 .450e .202 .166 .205661120000000
a. Predictors: (Constant), CCI B/W%, Stock B/W%, Debt % of GDP B/W%, Interest Rate B/W%, Unemployment B/W
%
b. Predictors: (Constant), CCI B/W%, Stock B/W%, Debt % of GDP B/W%, Interest Rate B/W%
c. Predictors: (Constant), CCI B/W%, Debt % of GDP B/W%, Interest Rate B/W%
d. Predictors: (Constant), Debt % of GDP B/W%, Interest Rate B/W%
e. Predictors: (Constant), Debt % of GDP B/W%
With model four providing significance, the combination of Debt as a percentage of GDP
combined with interest rate increases explained 19.3% of the variance based on the Adjusted R
Square value of 0.193. The significance related to all five predictive variables was not
significant. The null hypothesis was not rejected, and there was no support for the alternative
hypothesis. While two of the variables combined did provide predictability of revenue
performance at Cedar Fair parks, all five independent variables combined did not provide a
relationship to the dependent variable.
75
Table 4
Cedar Fair Revenue ANOVA Results
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression .404 5 .081 1.910 .142b
Residual .762 18 .042
Total 1.166 23
2 Regression .394 4 .098 2.419 .084c
Residual .773 19 .041
Total 1.166 23
3 Regression .357 3 .119 2.945 .058d
Residual .809 20 .040
Total 1.166 23
4 Regression .307 2 .154 3.755 .040e
Residual .859 21 .041
Total 1.166 23
5 Regression .236 1 .236 5.579 .027f
Residual .931 22 .042
Total 1.166 23
a. Dependent Variable: Rev B/W%
b. Predictors: (Constant), CCI B/W%, Stock B/W%, Debt % of GDP B/W%, Interest Rate B/W%,
Unemployment B/W%
c. Predictors: (Constant), CCI B/W%, Stock B/W%, Debt % of GDP B/W%, Interest Rate B/W%
d. Predictors: (Constant), CCI B/W%, Debt % of GDP B/W%, Interest Rate B/W%
e. Predictors: (Constant), Debt % of GDP B/W%, Interest Rate B/W%
f. Predictors: (Constant), Debt % of GDP B/W%
Research Question 3 results. The following is a restatement of research question Q3
and the associated null and alternative hypothesis.
Q3. To what extent, if any, is there a relationship between merging all five predictive
variables (consumer confidence index, stock market values, interest rates, unemployment rates,
and the consumer credit index) together and attendance at Six Flags theme parks?
76
H30. There is no relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and revenue performance at Cedar Fair theme parks at a statistically
significant level.
H3a. There is a relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and revenue performance at Cedar Fair theme parks at a statistically
significant level.
The relationship between the five predictive variables (consumer confidence index, stock
market values, interest rates, unemployment rates, and the consumer credit index) and attendance
at Six Flags theme parks was evaluated using a backwards regression model. The linear
regression summary reflected no correlation between the variables, R=1.0 (see Table 5).
Table 5
Six Flags Attendance Regression Results
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 1.000a 1.000 . .
a. Predictors: (Constant), CCI B/W%, Unemployment B/W%, Interest Rate B/W%, Debt % of GDP B/W%, Stock
B/W%
The ANOVA value was also displayed at zero, indicating that the R Square value did not
significantly predict the outcome variable of attendance at Six Flags parks (see Table 6). The
insignificance of the R Square value along with the lack of significance from the ANOVA model
failed to reject the null hypothesis. Published data pertaining to attendance at Six Flags parks
77
was limited to annual data, thus limiting the ability of the model to generate significant
correlations. There was no support for the alternate hypothesis with the null hypothesis not
being rejected. No relationship exists between all five predictive variables and attendance at Six
Flags parks.
Table 6
Six Flags Attendance ANOVA Results
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression .008 5 .002 . .b
Residual .000 0 .
Total .008 5
a. Dependent Variable: Attn B/W%
b. Predictors: (Constant), CCI B/W%, Unemployment B/W%, Interest Rate B/W%, Debt % of GDP B/W%,
Stock B/W%
Research Question 4 results. The following is a restatement of research question Q4
and the associated null and alternative hypothesis.
Q4. To what extent, if any, is there a relationship between merging all five predictive
variables (consumer confidence index, stock market values, interest rates, unemployment rates,
and the consumer credit index) together and revenue performance at Six Flags theme parks?
H40. There is no relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index) together and revenue performance at Six Flags theme parks at a statistically
significant level.
H4a. There is a relationship between merging all five predictive variables (consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
78
credit index) together and revenue performance at Six Flags theme parks at a statistically
significant level.
The relationship between the five predictive variables (consumer confidence index, stock
market values, interest rates, unemployment rates, and the consumer credit index) and revenue at
Six Flags theme parks was evaluated using a backwards regression model. The linear regression
summary reflected the best correlation between all five independent variables excluding only
Debt as a % of GDP in relation to revenue performance at Six Flags parks, R Square=.335 (see
Table 7). The other four models produced by the backwards regression model provided
significant values during the ANOVA analysis, but provided a lower R Squared total (see Table
7 and Table 8).
Table 7
Six Flags Revenue Regression Results
Model SummaryModel R R Square Adjusted R Square Std. Error of the Estimate
1 .683a .467 .319 .094753103100000
2 .671b .451 .335 .093614750400000
3 .636c .404 .315 .095025008400000
4 .598d .358 .297 .096267823900000
5 .524e .274 .241 .100000288000000
a. Predictors: (Constant), CCI B/W%, Stock B/W%, Debt % of GDP B/W%, Interest Rate B/W%, Unemployment
B/W%
b. Predictors: (Constant), CCI B/W%, Stock B/W%, Interest Rate B/W%, Unemployment B/W%
c. Predictors: (Constant), CCI B/W%, Stock B/W%, Unemployment B/W%
d. Predictors: (Constant), Stock B/W%, Unemployment B/W%
e. Predictors: (Constant), Stock B/W%
With model two providing significance and the largest adjusted R Squared value, all
independent variables excluding Debt as a percentage of GDP explained 33.5% of the variance
79
based on the Adjusted R Square value of 0.335. The significance related to all five predictive
variables was not significant. The null hypothesis was not rejected, and there was no support for
the alternative hypothesis. While four of the variables combined did provide predictability of
revenue performance at Six Flags parks, all five independent variables combined did not provide
the greatest relationship to the dependent variable.
Table 8
Six Flags Revenue ANOVA Results
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression .141 5 .028 3.152 .032b
Residual .162 18 .009
Total .303 23
2 Regression .137 4 .034 3.896 .018c
Residual .167 19 .009
Total .303 23
3 Regression .123 3 .041 4.522 .014d
Residual .181 20 .009
Total .303 23
4 Regression .108 2 .054 5.853 .010e
Residual .195 21 .009
Total .303 23
5 Regression .083 1 .083 8.310 .009f
Residual .220 22 .010
Total .303 23
a. Dependent Variable: Rev B/W%
b. Predictors: (Constant), CCI B/W%, Stock B/W%, Debt % of GDP B/W%, Interest Rate B/W%,
Unemployment B/W%
c. Predictors: (Constant), CCI B/W%, Stock B/W%, Interest Rate B/W%, Unemployment B/W%
d. Predictors: (Constant), CCI B/W%, Stock B/W%, Unemployment B/W%
e. Predictors: (Constant), Stock B/W%, Unemployment B/W%
f. Predictors: (Constant), Stock B/W%
80
Research Question 5 results. The following is a restatement of research question Q5
and the associated null and alternative hypothesis.
Q5. To what extent, if any, is there a covariance within the predictive variables consumer
confidence index, stock market values, interest rates, unemployment rates, and the consumer
credit index?
H50. There is no significant covariance within at least one of the predictive variables
consumer confidence index, stock market values, interest rates, unemployment rates, and the
consumer credit index.
H5a. There is a significant covariance within at least one of the predictive variables
consumer confidence index, stock market values, interest rates, unemployment rates, and the
consumer credit index.
To analysis the covariance between the five predictive variables an inner correlation
analysis was performed. The inter-item correlation between most of the variables was not
significant apart from three sets. Using the 2-tailed correlation factor, unemployment percentage
and debt as a percentage of GDP had a strong covariance. Unemployment percentage and
consumer confidence along with interest rates and consumer confidence both had strong
covariance between each set of data (see Table 9). The null hypothesis was rejected with support
for the alternative hypothesis. With more than one predictive variable showing covariance with
another predictive variable, the null hypothesis was rejected.
81
Table 9
Independent Covariance Analysis
CorrelationsDebt % of
GDP B/W%
Unemployme
nt B/W%
Interest Rate
B/W%
Stock B/W
% CCI B/W%
Debt % of GDP B/W
%
Pearson
Correlation
1 .607** -.362 .079 -.251
Sig. (2-tailed) .002 .083 .715 .236
N 24 24 24 24 24
Unemployment B/W
%
Pearson
Correlation
.607** 1 -.142 .008 -.508*
Sig. (2-tailed) .002 .507 .972 .011
N 24 24 24 24 24
Interest Rate B/W% Pearson
Correlation
-.362 -.142 1 .043 .502*
Sig. (2-tailed) .083 .507 .843 .013
N 24 24 24 24 24
Stock B/W% Pearson
Correlation
.079 .008 .043 1 .036
Sig. (2-tailed) .715 .972 .843 .869
N 24 24 24 24 24
CCI B/W% Pearson
Correlation
-.251 -.508* .502* .036 1
Sig. (2-tailed) .236 .011 .013 .869
N 24 24 24 24 24
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).
Evaluation of Findings
The study evaluated three main research questions related to the correlation between
macroeconomic factors and the two main regional theme parks in North America. The first
questions addressed the correlation between five macroeconomic factors and attendance and
each theme park location. The lack of published data around quarterly attendance at each theme
82
park limited the analysis providing no significant correlation between the macroeconomic
indicators and attendance.
The second main question evaluated revolved around the correlation between the same
five macroeconomic factors and the revenue performance for both Cedar Fair and Six Flags.
Each park had different results with both showing a correlation between specific macroeconomic
variables and their revenue performance. While the null hypothesis of all five macroeconomic
factors combined showing a correlation to revenue performance at Cedar Fair was rejected, the
combination of Debt as a percentage of GDP and Interest Rate growth represented 26.3% of the
variance in revenue performance during the timeframe evaluated. The standardized coefficients
beta showed that when Debt as a Percentage of GDP decreased, revenue at Cedar Fair parks
increased. These same coefficients displayed Cedar Fair revenues were shown to rise as Interest
Rates increased across the country.
When evaluating Six Flags revenue performance versus the five macroeconomic
variables, a different set of results were generated. While all five variables combined did not
generate the greatest correlation to the park’s revenue performance, all predictive variables
excluding Debt as a Percentage of GDP explained 45.1 percentage of the revenue variance
during the timeframe evaluated. While the null hypothesis was not rejected, most predictive
variables had a significant impact on revenue performance at Six Flags locations.
The last main question addressed evaluated the covariance between the predictive
variables. Three sets of variables had significant covariance between them. Unemployment
percentage and debt as a percentage of GDP, unemployment percentage and consumer
confidence, and interest rates and consumer confidence all had strong covariance between each
other. It is not surprising the unemployment impacts both debt levels and consumer confidence
83
levels. Interest rates have also been commonly linked to consumers’ confidence in the economy.
While these are significant, they do not impact the results of the study in a material manner.
Summary
The preceding chapter was designed to present the findings of the quantitative linear
regression correlation analysis study designed to answer give research questions to test the
corresponding null hypothesis. The first questions addressed the correlation between five
macroeconomic factors and attendance and each theme park location. The lack of published data
around quarterly attendance at each theme park limited the analysis providing no significant
correlation between the macroeconomic indicators and attendance.
The second main question evaluated revolved around the correlation between the same
five macroeconomic factors and the revenue performance for both Cedar Fair and Six Flags.
While the null hypothesis was rejected for both subjects, significant correlations existed between
specific macroeconomic factors and revenue performance for both companies. Understanding
the impact of specific macroeconomic indicators for each company individually was significant.
The last main question addressed evaluated the covariance between the predictive
variables. Three sets of variables had significant covariance between them. While the correlation
between these three sets of variables was significant, they did not impact the results of the study
in a material manner.
84
Chapter 5: Implications, Recommendations, and Conclusions
With consumer spending accounting for more than half of the gross domestic product in
the United States, understanding the impact of the consumer confidence index, stock market
values, interest rates, unemployment rates, and the consumer credit index on spending trends is
crucial to predicting reactions from changing macroeconomic conditions (Gaber, Gruevski, &
Gaber, 2013). Therefore, the specific problem evaluated in this study was how consumer
discretionary spending is impacted by macroeconomic trends, and more specifically, how these
macroeconomic factors impacted consumer spending at regional theme parks. The purpose of
this quantitative ex post facto study was to understand how macroeconomic indicators including
consumer confidence index, stock market values, interest rates, unemployment rates, and the
consumer credit index impacted regional theme park attendance and revenue during times of
different macroeconomic conditions.
Implications
Research conducted for the total travel and tourism industry cannot be generalized to the
regional theme park industry because no other industry is as reliant on consumer’s ability to
change their consumption, or attendance patterns on such a consistent basis (Cantor &
Rosentraub, 2012). This ability for customers to change plans throughout the operating season
impacts both attendance and revenue numbers for regional theme parks (Bakir & Baxter, 2011).
Due to the major impact that consumer spending has on regional theme park attendance and
revenue performance, the ability for regional theme parks to forecast consumers’ spending at
their parks based on macroeconomic trends will provide stakeholders with the information
needed to plan and proactively manage times of changing macroeconomic conditions. Current
85
research around macroeconomic impacts on consumer spending has excluded the impact on
regional theme park performance. Five implications were based upon the results driven by this
study. The following discussion revolves around the implications of each specific research
question and the associated hypotheses they addressed.
Implication 1. RQ1: To what extent, if any, is there a relationship between merging all
five predictive variables (consumer confidence index, stock market values, interest rates,
unemployment rates, and the consumer credit index) together and attendance at Cedar Fair theme
parks? The linear regression summary reflected no correlation between the variables. Published
data pertaining to attendance at Cedar Fair parks was limited to annual data, thus limiting the
ability of the model to generate significant correlations.
Implication 2. RQ2: To what extent, if any, is there a relationship between merging all
five predictive variables (consumer confidence index, stock market values, interest rates,
unemployment rates, and the consumer credit index) together and revenue performance at Cedar
Fair theme parks? A correlation between the independent variables Debt as a percentage of GDP
and Interest Rates in relation to revenue performance at Cedar Fair parks exists. These findings
are consistent with previous research indicating as a direct input into the amount of income that
is available for consumers to spend, the consumer credit index, or the availability of credit or
loans within the United States economy is a driving factor within research around consumer
spending (Bearden & Haws, 2012). These results also aligned with previous research indicating
that while decreases in interest rates did encourage future planned spending, it was not a driver
for increased spending on a day to day basis (Kandil & Mirzaie, 2011). When interest rates
lifted, or were predicted to increase, both cyclical and planned spending were shown to decrease
(Kandil & Mirzaie, 2011). The significance related to all five predictive variables was not
86
significant. While two of the variables combined did provide predictability of revenue
performance at Cedar Fair parks, all five independent variables combined did not provide a
relationship to the dependent variable. Understanding the correlation between Debt as a
percentage of GDP and Interest Rates in relationship to revenue performance at Cedar Fair parks
could allow for a better prediction of future revenue performance given specific macroeconomic
forecast related to these two indicators.
Implication 3. RQ3: To what extent, if any, is there a relationship between merging all
five predictive variables (consumer confidence index, stock market values, interest rates,
unemployment rates, and the consumer credit index) together and attendance at Six Flags theme
parks? The linear regression summary reflected no correlation between the variables. Published
data pertaining to attendance at Six Flags parks was limited to annual data, thus limiting the
ability of the model to generate significant correlations.
Implication 4. RQ4: To what extent, if any, is there a relationship between merging all
five predictive variables (consumer confidence index, stock market values, interest rates,
unemployment rates, and the consumer credit index) together and revenue performance at Six
Flags theme parks? The linear regression summary reflected the best correlation between all five
independent variables excluding only Debt as a % of GDP in relation to revenue performance at
Six Flags parks. These conclusions aligned with previous research indicating that the theory of
planned behavior also indicated the same trends, concluding that attitudes that are influenced in
the short-term drive decision making (Chambers et al., 2011). In relationship to the stock
market, these results coincided with previous research that concluded consumers see the stock
market as a leading indicator of future economic activity, thus giving the consumer the
expectation of financial growth or decline (Hsu, Lin, & Wu, 2011a). This data also indicated that
87
long term decisions like investments are not as impacted by shifts in consumer confidence,
where day to day consumer spending, on the other hand, is very tied to consumer confidence
levels (Heim, 2010b). These results also aligned with previous research indicating that while
decreases in interest rates did encourage future planned spending, it was not a driver for
increased spending on a day to day basis (Kandil & Mirzaie, 2011). When interest rates lifted, or
were predicted to increase, both cyclical and planned spending were shown to decrease (Kandil
& Mirzaie, 2011). Lastly, the conclusion aligned with the theory that as employment rates
decline, consumer spending will soon follow (Howard & Shipps, 2013). A lack of jobs in an
economy has been shown to lead to foreclosures, homelessness, and bankruptcies (Howard &
Shipps, 2013). All independent variables excluding Debt as a percentage of GDP explained
45.1% of the revenue variance for Six Flags locations. While other outside factors obviously play
a role in the revenue performance of the company, it is evident that these macroeconomic factors
can predict trends in advance of actual performance.
Implication 5. RQ5: To what extent, if any, is there a covariance within the predictive
variables consumer confidence index, stock market values, interest rates, unemployment rates,
and the consumer credit index? Unemployment percentage and debt as a percentage of GDP,
unemployment percentage and consumer confidence, and interest rates and consumer confidence
all had strong covariance between each sets of data. The sets of data were influenced by each
other, but not solely dependent on any one other factor.
Recommendations
The findings and implications of this study present multiple recommendations and
opportunities for further research. The following sections discusses practical methods for
88
regional theme parks to better predict attendance and revenue performance based on
macroeconomic trends and indicators that are published in advance.
Recommendation for practical applications in the field of the study. Based on the
findings of this study, regional theme parks should utilize specific macroeconomic indicators to
predict future performance at their locations. The applications are limited to revenue
performance at this point due to the limited attendance information that is publicly released.
With full access to the attendance data, along with other internal factors, regional theme park
operators should be better equipped to plan resources over the short and long run using this
process.
Recommendations for future research. Variables included in this study did not prove
correlations between all economic indicators and regional theme park performance. This lack of
correlation between all indicators should not prevent future research by the industry related to
this topic. The correlations shown should provide motivation for further research into these areas
of correlation that exist. Further research could focus on additional economic variables in
addition to a deeper analysis related to attendance given full access to monthly attendance
numbers. Lastly, a clean look at the data with the external factors eliminated from the study like
weather, time of year, capital investments, and one off impacts could provide exponential
amounts of useful information if used correctly.
Conclusions
The purpose of this quantitative ex post facto study was to understand how
macroeconomic indicators including consumer confidence index, stock market values, interest
89
rates, unemployment rates, and the consumer credit index impacted regional theme park
attendance and revenue during times of different macroeconomic conditions. Five research
questions were answered using quantitative methods of correlation, linear and multiple
regression, and ANOVA. The study evaluated the theory of planned behavior in relation to
attendance and revenue performance at regional theme parks in the United States. The results of
this study suggest that there are specific macroeconomic indicators that do predict future
performance within the regional theme park industry.
The study provided evidence for three main implications. The first implication, the use of
macroeconomic indicators to predict attendance at regional theme parks is restricted due to the
lack of public monthly attendance numbers. The second implication, specific macroeconomic
indicators can be used to predict future regional theme park revenue performance. The third
implication, specific macroeconomic indicators had intra correlations that must be considered
during research. Further research could be conducted to find other macroeconomic variables that
impact regional theme park performance. The study contributed to the theory of planned
behavior by presenting a correlation between macroeconomic indicators and consumers future
spend with regional theme parks across the United States.
90
References
Abaidoo, R. (2014). Private and public investment growth: Macroeconomic expectations and
fiscal policy uncertainty. International Business Research, 7(1), 116-129.
doi:http://dx.doi.org/10.5539/ibr.v7n1p116
Abaidoo, R. (2015). "Global" productivity trends, consumption expenditures and US
macroeconomic conditions: A verification of the "contagion" phenomenon. International
Business Research, 8(5), 55-65. doi: http://dx.doi.org/10.5539/ibr.v8n5p55
Arora, V. (2016). Consumer credit, oil prices, and the U.S. economy. Turkish Economic Review,
3(1), 122-142. doi:http://dx.doi.org.proxy1.ncu.edu/10.1453/ter.v3i1.629
Baek, E., & DeVaney, S. A. (2010). How do families manage their economic hardship? Family
Relations, 59(4), 358-368. doi: http://dx.doi.org/10.1111/j.1741-3729.2010.00608.x
Bakir, A., & Baxter, S. G. (2011). 'Touristic fun': Motivational factors for visiting legoland
windsor theme park. Journal of Hospitality Marketing & Management, 20(3), 407-424.
doi:10.1080/19368623.2011.562431
Barello, S. H. (2014). Consumer spending and U.S. employment from the 2007-2009 recession
through 2022. Monthly Labor Review, , 1-36. Retrieved from http://proxy1.ncu.edu/login?
url=http://search.ebscohost.com/login.aspx?
direct=true&db=bth&AN=99408721&site=ehost-live
91
Barnes, M. L. 1., michelle.barnes@bos.frb.org, & Olivei, G. P. 2., giovanni.olivei@bos.frb.org.
(2013). The michigan surveys of consumers and consumer spending. Research Review, (20),
104-107. doi: http://www.bostonfed.org/economic/ppb/2013/ppb138.htm
Bearden, W. O., & Haws, K. L. (2012). How low spending control harms consumers. Academy
of Marketing Science.Journal, 40(1), 181-193. doi:
http://dx.doi.org.proxy1.ncu.edu/10.1007/s11747-011-0282-1
Bhuyan, S. (2011). Do consumers' attitudes and preferences determine their FAFH behavior? an
application of the theory of planned behavior. Agribusiness, 27(2), 205-220.
doi:10.1002/agr.20256
BRUNER, C. M. (2010). The changing face of money. Review of Banking & Financial Law,
30(1), 383-406.
Bryant, W. D. A., & Macri, J. (2005). DOES SENTIMENT EXPLAIN CONSUMPTION?
Journal of Economics and Finance, 29(1), 97-111. doi: http://dx.doi.org/10.1007/bf02761545
Byun, J., & Jang, S. (. (2015). Effective promotions for membership subscriptions and renewals
to tourist attractions: Discount vs. bonus. Tourism Management, 50, 194-203.
doi:10.1016/j.tourman.2015.02.002
Cantor, M. B., & Rosentraub, M. S. (2012). Are gaming and sport effective tourism strategies
during economic contractions? evidence from the performance of baseball and casinos during
america's great recession. Journal of Sport & Tourism, 17(1), 23-42. doi:
http://dx.doi.org/10.1080/14775085.2012.657389
92
Caporale, G. M., Gil-Alana, L., & Lovacha, Y. (2016). Testing unemployment theories: A
multivariate long memory approach. Journal of Applied Economics, 19(1), 95-112. Retrieved
from http://proxy1.ncu.edu/login?url=http://search.ebscohost.com/login.aspx?
direct=true&db=bth&AN=116477688&site=ehost-live
Carter, R. (2014). A multiple regression analysis: Fiscal strategies and unemployment rates
Franklin Publishing Company.
Carter, R. (2015). A multiple regression analysis fiscal strategies and unemployment rates.
Insights to a Changing World Journal, 2015(1), 95-169.
Catte, P., Cova, P., Pagano, P., & Visco, I. (2011). The role of macroeconomic policies in the
global crisis. Journal of Policy Modeling, 33(6), 787-803.
doi:10.1016/j.jpolmod.2011.06.001
Cedar Fair Entertainment. (2015). 2015 Annual Report of Cedar Fair Entertainment. retrieved
from http://www.mergentonline.com.proxy1.ncu.edu/documents.php?compnumber=51134
Chambers, V., Benibo, B. R., & Spencer, M. (2011). REACTIONS TO THE 2008 ECONOMIC
CRISIS AND THE THEORY OF PLANNED BEHAVIOR. Academy of Accounting and
Financial Studies Journal, 15(4), 17-30.
Cooper, D., daniel.cooper@bos.frb.org, & Dynan, K., kdynan@brookings.edu. (2013). Wealth
shocks and macroeconomic dynamics. Research Review, (19), 13-18. doi:
http://dx.doi.org/10.2139/ssrn.2321396
93
Cornelis, P. C. M. (2011). A management perspective on the impact of new attractions. Journal
of Vacation Marketing, 17(2), 151-162. doi:10.1177/1356766710392483
Cynamon, B. Z., & Fazzari, S. M. (2013). Rising inequality, recession and slow recovery: A sad
american tale. Intereconomics, 48(6), 379-380.
doi:http://dx.doi.org.proxy1.ncu.edu/10.1007/s10272-013-0481-8
DeLisle, J. R. 1., jrdelisle@jrdelisle.com. (2013). A positive state of mind. Appraisal Journal,
81(2), 99-111. doi:
http://jrdelisle.com/market_update/finviews/TAJ_SP13_FinancialViews_F.pdf
Duarte Alonso, A., Sakellarios, N., & Cseh, L. (2015). The theory of planned behavior in the
context of a food and drink event: A case study. Journal of Convention & Event Tourism,
16(3), 200-227. doi:10.1080/15470148.2015.1035822
Durante, K. M., & Laran, J. (2016). The effect of stress on consumer saving and spending.
Journal of Marketing Research (JMR), 53(5), 814-828. doi:10.1509/jmr.15.0319
Dynan, K. (2012). Is a household debt overhang holding back consumption? Brookings Papers
on Economic Activity, (1), 299-362. doi: http://dx.doi.org/10.1353/eca.2012.0001
Eastman, J. K., McKay, B. P., & Forehand, J. (2010). Examining retailer and consumer
perceptions in determining economic expectations: A demonstration with the 2008 holiday
season. Journal of Applied Business Research, 26(4), 21-34. doi:
http://dx.doi.org/10.19030/jabr.v26i4.303
94
Ferrer, E., Salaber, J., & Zalewska, A. (2016). Consumer confidence indices and stock markets'
meltdowns. European Journal of Finance, 22(3), 195-220.
doi:10.1080/1351847X.2014.963634
Florea (Moise), O. (2014). The influence of employment rate and unemployment benefits on
household expenses. International Journal of Academic Research in Economics and
Management Sciences, 3(5), 1-10. doi: http://dx.doi.org/10.6007/ijarems/v3-i5/1139
Fullwiler, S. T. (2007). Interest rates and fiscal sustainability. Journal of Economic Issues, 41(4),
1003-1042. doi: http://dx.doi.org/10.1080/00213624.2007.11507085
Gaber, S., Gruevski, I., & Gaber, V. (2013). The effects of discretionary fiscal policy on
macroeconomic aggregates. Business & Economic Horizons, 9(1), 32-39. doi:
http://dx.doi.org/10.15208/beh.2013.4
Gatti, D. (2009). Public spending, market imperfections, and unemployment. Eastern Economic
Journal, 35(4), 452-461. doi: http://dx.doi.org.proxy1.ncu.edu/10.1057/eej.2008.44
Gausden, R., & Hasan, M. S. (2016). Would information on consumer confidence have helped to
predict UK household expenditure during the recent economic crisis? Applied Economics,
48(18), 1695-1709. doi:10.1080/00036846.2015.1105926
Gomes, O. (2010). Consumer confidence, endogenous growth and endogenous cycles. Journal of
Economic Studies, 37(4), 377-404. doi:
http://dx.doi.org.proxy1.ncu.edu/10.1108/01443581011073390
95
Heim, J. J. (2010a). The impact of consumer confidence on consumption and investment
spending. The Journal of Applied Business and Economics, 11(2), 37-54. doi:
http://dx.doi.org/10.1016/0148-6195(83)90006-1
Howard, R. L., & Shipps, B. P. (2013). The new economic reality and the unemployment rate:
Will it ever get below 5% again? The Journal of Applied Business and Economics, 14(3), 83-
107. doi: http://www.na-businesspress.com/JABE/HowardRL_Web14_3_.pdf
Hsu, C., Lin, H., & Wu, J. (2011a). Consumer confidence and stock markets: The panel causality
evidence. International Journal of Economics and Finance, 3(6), 91-98. doi:
http://dx.doi.org/10.5539/ijef.v3n6p91
Ichiue, H., & Nishiguchi, S. (2015). Inflation expectations and consumer spending at the zero
bound: Micro evidence. Economic Inquiry, 53(2), 1086-1107. doi:10.1111/ecin.12176
ISMIHAN, M., & OZKAN, F. G. (2011). The political economy of public spending decisions
and macroeconomic performance. International Journal of Economic Perspectives, 5(2),
163-174. doi: http://search.proquest.com.proxy1.ncu.edu/docview/1021962159/fulltextPDF/
84E343C99448432APQ/1?accountid=28180
Jain, S., Khan, M. N., & Mishra, S. (2015). Factors affecting luxury purchase intention: A
conceptual framework based on an extension of the theory of planned behavior. South Asian
Journal of Management, 22(4), 136-163. doi: http://dx.doi.org/10.6007/ijarbss/v4-i4/808
Jarde, A., Losilla, J. M., & Vives, J. (2012). Suitability of three different tools for the assessment
of methodological quality in ex post facto studies. International Journal of Clinical Health &
96
Psychology, 12(1), 97-108. doi: http://docplayer.net/711596-Suitability-of-three-different-
tools-for-the-assessment-of-methodological-quality-in-ex-post-facto-studies-1.html
Johnson, M. A., & Naka, A. (2014). Downside risk: What the consumer sentiment index reveals.
Financial Services Review, 23(1), 45-61. doi: http://dx.doi.org/10.2139/ssrn.1928906
Jung, T. L. (2016). Research on taiwan theme parks' experience marketing strategy and revisit
willingness, purchase willingness and recommendation willingness. International Journal of
Organizational Innovation, 9(1), 35-53. Retrieved from http://proxy1.ncu.edu/login?
url=http://search.ebscohost.com/login.aspx?
direct=true&db=bth&AN=116828286&site=ehost-live
Kandil, M., & Mirzaie, I. A. (2011). Consumption, credit, and macroeconomic policies: Theory
and evidence from the united states. Global Economic Review, 40(3), 323. doi:
http://dx.doi.org/10.1080/1226508x.2011.601645
Kilic, E., & Cankaya, S. (2016). Consumer confidence and economic activity: A factor
augmented VAR approach. Applied Economics, 48(32), 3062-3080.
doi:10.1080/00036846.2015.1133902
Kwan, A. C. C., & Cotsomitis, J. A. (2006). THE USEFULNESS OF CONSUMER
CONFIDENCE IN FORECASTING HOUSEHOLD SPENDING IN CANADA: A
NATIONAL AND REGIONAL ANALYSIS. Economic Inquiry, 44(1), 185-197. doi:
http://dx.doi.org/10.1093/ei/cbi064
97
Kyoung, T. K., & Hanna, S. D. (2016). The impact of the 2008-2009 stock market crash on the
wealth of U.S. households. Journal of Financial Planning, 29(2), 54-60. Retrieved from
http://proxy1.ncu.edu/login?url=http://search.ebscohost.com/login.aspx?
direct=true&db=bth&AN=112842997&site=ehost-live
Li, J. F., & Lin, Z. X. (2016). Social benefit expenditures and stagflation: Evidence from the
united states. Applied Economics, 48(55), 5340-5347. doi:10.1080/00036846.2016.1176118
Ma, Y., Ailawadi, K. L., Gauri, D. K., & Grewal, D. (2011). An empirical investigation of the
impact of gasoline prices on grocery shopping behavior American Marketing Association.
doi:10.1509/jmkg.75.2.18
McCarthy, J., & Steindel, C. (2007a). Housing activity and consumer spending. Business
Economics, 42(2), 6-21. doi: http://dx.doi.org/10.2145/20070201
Merwe, K., & Maree, T. (2016). The behavioural intentions of specialty coffee consumers in
south africa. International Journal of Consumer Studies, 40(4), 501-508.
doi:10.1111/ijcs.12275
Nnadi, M. (2011). The impact of fiscal policies on consumers' spending. Journal of Economics
and International Finance, 3(1), 59-62. doi:
http://www.academicjournals.org/journal/JEIF/article-abstract/24083114525
Parker, J. A., Souleles, N. S., Johnson, D. S., & McClelland, R. (2013). Consumer spending and
the economic stimulus payments of 2008. The American Economic Review, 103(6), 2530-
2553. doi: http://dx.doi.org.proxy1.ncu.edu/10.1257/aer.103.6.2530
98
Perles-Ribes, J., Ramón-Rodríguez, A. B., Sevilla-Jiménez, M., & Moreno-Izquierdo, L. (2016).
Unemployment effects of economic crises on hotel and residential tourism destinations: The
case of spain. Tourism Management, 54, 356-368. doi:10.1016/j.tourman.2015.12.002
Poterba, J. M. (2000). Stock market wealth and consumption. The Journal of Economic
Perspectives, 14(2), 99-118. doi: http://dx.doi.org/10.1257/jep.14.2.99
Redmond, W. H. (2001). Exploring limits to material desire: The influence of preferences vs.
plans on consumption spending. Journal of Economic Issues, 35(3), 575-589. doi:
http://dx.doi.org/10.1080/00213624.2001.11506391
Salamat, U. B., & Banik, S. (2013a). Amusement marketing: A few dimensions of amusement
parks. International Journal of Business Insights & Transformation, 7(1), 36-41. doi:
http://eds.a.ebscohost.com.proxy1.ncu.edu/ehost/pdfviewer/pdfviewer?sid=c8d2f622-aebd-
4608-9e10-26949cf56cfc%40sessionmgr4003&vid=1&hid=4113
Schooley, D. K., & Worden, D. D. (2010). Fueling the credit crisis: Who uses consumer credit
and what drives debt burden? Business Economics, 45(4), 266-276. doi:
http://dx.doi.org.proxy1.ncu.edu/10.1057/be.2010.25
SeaWorld Entertainment Inc. (2015). 2015 Annual Report of SeaWorld Entertainment Inc.
retrieved from http://www.mergentonline.com.proxy1.ncu.edu/documents.php?
compnumber=136534
Shaffer, S., & Zuniga, B. (2016). Consumer debt and unemployment. Applied Economics
Letters, 23(17), 1250-1252. doi:10.1080/13504851.2016.1148250
99
Six Flags Entertainment Corporation. (2015). 2015 Annual Report of Six Flags Entertainment
Corporation retrieved from http://www.mergentonline.com.proxy1.ncu.edu/documents.php?
compnumber=8249
Sum, V. (2014). EFFECTS OF BUSINESS AND CONSUMER CONFIDENCE ON STOCK
MARKET RETURNS: CROSS-SECTIONAL EVIDENCE. Economics, Management and
Financial Markets, 9(1), 21-25. doi: http://dx.doi.org/10.2139/ssrn.2117679
Van Oest, R. D., Van Heerde, H. J., Dekimpe, M. G., Joo, H. H., Kang, H. G., & Moon, J. J.
(2014). Return on roller coasters: A model to guide investments in theme park attractions
INFORMS: Institute for Operations Research. doi:10.1287/mksc.1090.0553
Van Zandweghe, W., & Braxton, J. C. (2013). Has durable goods spending become less sensitive
to interest rates? Economic Review (01612387), , 5-27. doi:
https://www.kansascityfed.org/publicat/econrev/pdf/13q4VanZandweghe-Braxton.pdf
Velickovic, D., & Velickovic, J. (2016). Interest ratest and growt in developing countries.
Ekonomika, 62(2), 61-70.
doi:http://dx.doi.org.proxy1.ncu.edu/10.5937/ekonomika1602061V
Willis, J. L. 1., & Cao2, G. (2015). Has the U.S. economy become less interest rate sensitive?
Economic Review (01612387), 100(2), 5-36. doi:
https://www.kansascityfed.org/~/media/files/publicat/econrev/econrevarchive/
2015/2q15willis.pdf
100
Yerex, R. P. (2011a). The consumer-driven economy at a crossroads. Business Economics, 46(1),
32-42. doi: http://dx.doi.org.proxy1.ncu.edu/10.1057/be.2010.40
Yerex, R. P. (2011b). The consumer-driven economy at a crossroads. Business Economics,
46(1), 32-42. doi: http://dx.doi.org.proxy1.ncu.edu/10.1057/be.2010.40
Zoellner, J., Krzeski, E., Harden, S., Cook, E., Allen, K., & Estabrooks, P. A. (2012). Qualitative
application of the theory of planned behavior to understand beverage consumption behaviors
among adults. Journal of the Academy of Nutrition & Dietetics, 112(11), 1774-1784.
doi:10.1016/j.jand.2012.06.368