christopher peak final disseration 12-13-16 approved

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

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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

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?



and the consumer credit index) together and revenue performance at Cedar Fair theme parks?



and the consumer credit index) together and attendance at Six Flags theme parks?



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


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





credit index) together and attendance at Six Flags theme parks at a statistically significant level.






credit index) together and revenue performance at Cedar Fair theme parks at a statistically

significant level.



10


significant level.



credit index) together and revenue performance at Six Flags theme parks at a statistically

significant level.




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



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,


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.

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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

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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).

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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

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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

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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

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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

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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).

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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).

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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).

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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.

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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:










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credit index?
















significant level.

61




significant level.




significant level.




significant level.







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


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


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,


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


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




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,


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


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.










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













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


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%






76




significant level.




significant level.


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


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%









significant level.



78


significant level.


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


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





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 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.



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.



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.



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



89


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., [email protected], & Olivei, G. P. 2., [email protected].

(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?


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., [email protected], & Dynan, K., [email protected]. (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., [email protected]. (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?


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?


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

christopher peak final disseration 12-13-16 approved

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