Consumer Credit Behavior in Times of Crisis: A Natural Experiment Master Thesis University of Maastricht – School of Business and Economics 22.07.2010 Supervisor: Drs. ir. Nikos Kalogeras Second Evaluator: Drs. Theo Benos Study: IB Finance Hannah Friedrich, i337471

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Acknowledgments

I would like to thank my family and friends and all people who supported me during this

thesis and my studies at University Maastricht. First of all, I want to thank my parents. They

supported me throughout my whole study and gave me advice whenever needed. I am also

grateful to Mirko Jansen for his patience and understanding during the last month. In

particular I thank him for the valuable input and revision of this thesis.

I owe special thanks, to all who participated in this study and took time for the interviews.

Lastly, I would like to say thank you to my supervisor Nikos Kalogeras for his assistance. He

provided guidance throughout the whole process and advised me whenever requested.

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Abstract

This research deals with consumer credit behavior in times of crisis. In view of the financial

crisis, it is important to identify the factors that drive consumer behavior. Practitioners as well

as academics need to adjust their strategies to avoid damage and losses in times of crisis.

Therefore, it is important to know whether the primary factors risk attitude, risk perception,

their interaction effect, or trust drive the reduction of credit card transaction volume.

Additionally, it is of great value to identify the secondary factors that drive risk behavior: age,

gender, nationality, education, income and trust. Data is collected via a survey among

consumers. By applying a binary logistic regression, in a first step, and ordinary least squares

regressions, in a second step, the data is analyzed. The research yields that consumer behavior

in times of crisis is affected by risk perception. Moreover, the research yields that the

secondary factors affecting risk behavior are: age and nationality.

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Table of Content

Acknowledgments ...................................................................................................................... 1

Abstract ...................................................................................................................................... 2

List of Tables .............................................................................................................................. 5

List of Figures ............................................................................................................................ 5

1. Introduction ............................................................................................................................ 6

1.1 Introduction ................................................................................................................................... 6

1.2. Motivation .................................................................................................................................... 8

1.3. Research Question and Sub-Questions ....................................................................................... 10

1.4. Outline ........................................................................................................................................ 13

2. Individual Behavior in Economic Crisis and the Credit Card Segment: A selective

Literature Review ..................................................................................................................... 14

2.1. Introduction ................................................................................................................................ 14

2.2. Payment methods ....................................................................................................................... 14

2.3. Behavioral finance: Risk Behavior of consumers, market anomalies and crisis events ............. 15

2.3.1. Behavioral Finance and Consumer Risk Behavior................................................. 15

2.3.2. Crisis events ........................................................................................................... 21

2.3.3. Credit Cards and Consumer behavior .................................................................... 22

2.4. Risk Attitude............................................................................................................................... 24

2.4.1. Risk Attitude in general .......................................................................................... 24

2.4.2. Findings on Risk Attitude ...................................................................................... 27

2.5. Risk Perception........................................................................................................................... 30

2.5.1. Risk Perception in general ...................................................................................... 30

2.5.2. Findings on Risk Perception .................................................................................. 32

2.6. Trust ........................................................................................................................................... 34

2.7. Socioeconomic factors ............................................................................................................... 35

2.7.1. Age, Nationality & Gender .................................................................................... 35

2.7.2. Education & Income ............................................................................................... 36

2.8. Conclusion .................................................................................................................................. 37

3. Conceptual Model ................................................................................................................ 38

3.1. Introduction ................................................................................................................................ 38

3.2. Conceptual Model ...................................................................................................................... 38

3.3. Hypotheses ................................................................................................................................. 39

3.4. Graphical Demonstration of the conceptual Model .................................................................... 43

4. Research Design ................................................................................................................... 44

4.1. Introduction ................................................................................................................................ 44

4.2. Decision Context ........................................................................................................................ 44

4.2.1. The economic crisis ................................................................................................ 44

4.3. Research design .......................................................................................................................... 45

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4.4. Data Collection ........................................................................................................................... 46

4.5. Sample Design ............................................................................................................................ 46

4.5.1. Relevant sample population ................................................................................... 46

4.5.2. Sampling frame ...................................................................................................... 47

4.5.3. Sampling technique ................................................................................................ 47

4.5.4. Sample Size ............................................................................................................ 47

4.6. Questionnaire Design ................................................................................................................. 48

4.6.1. Questionnaire Type ................................................................................................ 48

4.6.2. Data-collection approach ........................................................................................ 48

4.6.3. Questionnaire Content ............................................................................................ 48

4.6.4. Pre-test .................................................................................................................... 50

5. Results and Discussion ......................................................................................................... 51

5.1. Introduction ................................................................................................................................ 51

5.2. Data analysis............................................................................................................................... 51

5.2.1. Descriptive Statistics .............................................................................................. 51

5.2.2. Knowledge and Usage of credit cards and the crisis .............................................. 53

5.2.3. Factor Analysis and Data Validity ......................................................................... 55

5.2.4. Data Reliability ...................................................................................................... 60

5.3. Statistical analysis and Results ................................................................................................... 61

5.3.1. Logistic Regression ................................................................................................ 61

5.3.2. OLS Regressions on RA and RP ............................................................................ 67

5.4. Conclusion .................................................................................................................................. 70

6. Conclusion ............................................................................................................................ 73

6.1. Main Findings............................................................................................................................. 73

6.2. Theoretical Contribution ............................................................................................................ 74

6.3. Practical Contribution ................................................................................................................. 75

6.4. Limitations and Future Research ................................................................................................ 76

6.5. Concluding Remarks .................................................................................................................. 78

Reference List .......................................................................................................................... 79

Appendix 1 ............................................................................................................................... 85

English Questionnaire ....................................................................................................................... 85

German Questionnaire ....................................................................................................................... 88

Appendix 2 ............................................................................................................................... 92

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List of Tables

Table 1 Questions on Credit Card Usage and Knowledge ....................................................... 53

Table 2 Sources of Information for consumers ........................................................................ 55

Table 3 Factor Loadings before and after Varimax Rotation ................................................... 60

Table 4 Data Reliability ........................................................................................................... 61

Table 5 Logistic Model Summary Model Fit ........................................................................... 64

Table 6 Classification Table – Goodness of Fit ....................................................................... 64

Table 7 Logistic Regression Results – Regression Coefficients .............................................. 65

Table 8 OLS Regressions – Model Fit ..................................................................................... 68

Table 9 OLS Regression Results – Regression Coefficients ................................................... 68

Table 10 Logistic Regression- Hypotheses Overview ............................................................. 71

Table 11 OLS Regressions – Hypotheses Overview ............................................................... 72

List of Figures

Figure 1 Conceptual Model ...................................................................................................... 43

Figure 2 Gender Distribution ................................................................................................... 52

Figure 3 Age Distribution ........................................................................................................ 52

Figure 4 Educational Distribution ............................................................................................ 53

Figure 5 Knowledge on Payment Methods .............................................................................. 54

Figure 6 Concerns on Credit Card Usage ................................................................................. 54

Figure 7 Payment Preference ................................................................................................... 55

Figure 8 Check for normality ................................................................................................... 92

Figure 9 Check for normality ................................................................................................... 93

Figure 10 Check for homoscedasticity and linearity ................................................................ 93

Figure 11Check for normality .................................................................................................. 94

Figure 12Check for normality .................................................................................................. 94

Figure 13 Check for homoscedasticity and linearity ................................................................ 95

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1. Introduction

1.1 Introduction

The fact is that we are experiencing an economic crisis. Companies are facing rough waters

and consumers face lower wages and unemployment. Fact is consumers are struck hard by

this crisis. They did not expect another bubble to burst after the internet bubble in the early

2000s. However, this did happen. The mortgage bubble burst and hit the world economy. In

these times we have to deal with the situation and recover from it. Yet, one question remains.

Is another bubble - “The Credit Card Debt Bubble” - to burst? Many newspapers such as

Financial Times, the New York Times, and Der Spiegel published articles about the danger of

credit card debt. In fall 2008 it appeared that 70% of the US GDP is financed via private

consumption. This is, however, build upon $950 billion credit card debt, as American

households are used to live their lives on credit (Tigges, 2008).

During the summer of 2009, European banks feared that the credit card debt crisis spills over

from the US. As more and more people all over the world default on their debt, become

unemployed, and declare insolvency, Europe is endangered as well. An interesting fact is the

comparison of German and American citizens. Whereas Germans on average safe about ten

percent of their income each month, American citizens on average safe zero percent

(Woolsey, et al., 2009). The New York Times (2008) indicated that credit card users are

revising their habits. Consumers are less willing to rely on their credit cards and purchase less

with their credit cards. A study by Deutsche Bundesbank (2009) researches consumer

behavior towards means of payment. The results show a socioeconomic and demographic

segmentation of the credit card user market. Older people, less educated people and those who

have a lower income in Germany are using credit cards much less. Furthermore, the study

indicates that in Germany cash payment will continue to be the main payment method

(Deutsche Bundesbank, 2009). A US study from July 2008 states that 37% of US consumers

use their credit card less often. Due to the crisis consumers in the US show a cautious

behavior and cut back on their credit card usage. Nonetheless, consumers’ reaction is not the

only cause for diminishing credit card usage (Business Wire, 2008). Suppliers of banking

cards are reviewing their current strategies and offerings.

The last year’s (2009’s) figures showed that lenders are suffering from the high default risk of

credit card users (Spiegel, 2009). Credit card issuers are dealing with these problems. Raising

interest rates higher and higher it is possible to compensate for the default losses. Moreover,

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by shortening credit lines and reducing the number of credit cards issued, banks and providers

try to reduce losses (Spiegel, 2009).

Only American Express had to announce bad results for the second quarter of 2009 as

compared to the year before. Concerning the US card services segment, the company reported

a $200 million loss for the second quarter. In other segments including the international card

service, American Express was able to maintain an income even though the figures are lower

compared to the year before. The company perceives this as an obstacle it is well able to

overcome. “Despite continued weakness in card member spending and historically high levels of

loan losses, we generated $342 million of earnings from continuing operations, strengthened our

capital base and further expanded our deposit gathering initiatives.” (American Express, 2009)

For its big competitor Visa Inc. the quarter turned out to be better: “Despite the challenging

economy, Visa continued to post strong operational and financial performance during our

fiscal second quarter, and we remain confident in delivering our EPS guidance for FY 2009,”

said Joseph Saunders, Chairman and Chief Executive Officer of Visa Inc. (Visa Inc., 2009). In

spite of the shrinking turnover on credit cards in the US the company succeeded in the effort to

increase earnings with its international business. A similar picture follows for MasterCard

Worldwide. The company was able to increase earnings due to clever cost cutting actions: “We

are very pleased with our second-quarter financial performance and are adapting well to the

challenging economic environment,” said Robert W. Selander, MasterCard president and

chief executive officer. “The thoughtful actions we've taken to realign our resources and

priorities to match customer and local market needs, as well as our sharp focus on expense

management, have enabled us to deliver strong operating margin and net income

improvements.” (MasterCard Worldwide, 2009)

Over the past year, several headlines have been published through daily news and talk-shows

about the economic crisis stating that finally countries are dealing and defeating the recession.

According to Reuters the G7 are now past the worst phase and recovering (Heller, et al.,

2009). Yet, some critics state that this is not the case, which confuses consumers as well as

myself about the current economic situation the world is facing. Especially in Germany the

press states still that the recession is not over and companies and banks are still struggling

with the economic situation. This is further accompanied when considering that several

countries are struggling. Moreover, one can see that the recession is still in progress as

currencies are depreciating. Especially in Europe the EURO is depreciating substantially due

to countries facing too high debts and failing on these (Jung, et al., 2010). Therefore, it is

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interesting to see, whether the crisis has changed consumer behavior with respect to credit.

Analyzing consumer risk behavior and influencing factors in times of crisis is crucial to adjust

the system and strategies for the future. However, this will be elaborated in the following

chapter on the theoretical and managerial contribution of my research.

1.2. Motivation

Consumer behavior is a well researched topic in the context of marketing, and beginning to be

of greater importance in the context of finance. In order to improve strategies by politicians

and practitioners, gaining an understanding of individuals’ behavior and needs is crucial to

effectively deal with consumers. Especially in crisis events individual behavior differs and

leads to panic and market anomalies. Therefore, a study on the factors driving individual

behavior in times of financial and economic crisis helps in applying the right measures and

policies with an informative background. In order to mitigate the aftermath of the crisis, avoid

bankruptcies and to enhance recovery, an understanding of individual behavior appears to be

ever more important.

In case consumer credit behavior depends on risk attitude and/or risk perception, managers,

academics and politicians would be able to set up a system fitting better to these needs and

complying to the standards as well. To my knowledge there does not exist a study inspecting

the behavior of consumers in light of transaction volume by means of credit cards after a

crisis has hit the market and consumers. For this reason I want to research whether there is a

difference and why there is a difference in the credit card transaction volume after the crisis

from 2008 has hit the market. I believe that this research is of interest for the academic and

behavioral finance stream and may help retail banking and managers to consider marketing

strategies that fit their consumers. Moreover, politicians, under pressure to change “the rules

of the game”, get more insight and can react to the crisis in a better way.

The factors risk attitude and risk perception that I relate to transaction volume, will be applied

in a similar framework of Pennings et al (2002). Whereas risk attitude denotes how a

consumer interprets the content of risk and by how much the individual likes or dislikes the

risk content. It therefore, reflects the general predisposition to risk the consumer holds in a

consistent way. Risk perception in contrast reflects a consumer’s interpretation of the chance

to be exposed to risk. It denotes the assessment of the uncertainty of the risk content in a

particular situation and resembles the personal interpretation of the chance to be exposed to

the content of the risk. The two factors thus imply what actions can be taken to reduce or

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eliminate the risk. If risk attitude dominates an individual’s change in consumption by means

of credit cards, only risk elimination can change this attitude. Hence, the system would need

to change in order to reduce consumers’ fear of bad credit and personal insolvency due to

redundancy and behavioral risk factors. In case risk perception dominates the change in

consumption by means of credit card, better communication can enhance perception and

purchasing behavior. Practitioners then have to restore trust to advice consumers in the best

way and inform them of the diverse credit card types and usage. This is achieved optimally by

defining the risk tolerance of consumers and adjusting products and services to the individual

risk tolerance level. Moreover, the study offers retailers advice in finding the optimal payment

system to maximize customer satisfaction and purchases (Hirschman, 1979).

In addition to analyzing risk perception and risk attitude of consumers, I want to find out the

influence of trust on consumer behavior. This factor will be included in the first step of the

analysis as well as in the second step. In the second step I want to identify by which factors

risk behavior is driven. This helps practitioners to enhance communication about financial

products and increase customer satisfaction.

A first impact on risk behavior may be derived from the observable factors of age, gender and

income. It is well documented that women behave different than men when making decisions

under risk. Moreover, in line with the life cycle hypothesis, age can influence behavior as well

as the level of income an individual has for consumption. Besides these factors education and

nationality play a role in the level of risk tolerance and will be analyzed in the study. It is well

known that different country specific mentalities and cultures lead to diverse behavior and

dissimilar reactions to a crisis. In addition, risk behavior may depend on trust a consumer has

in the specific product and service and her experience within this sector.

In light of the theoretical contribution, my study offers an addition to the controversial

discussion between traditional and behavioral finance. In times of crisis, traditional finance

explains consumer behavior with the help of anomalies, whereas behavioral finance

developed theories to explain irrationality. Moreover, existing literature is extended with a

study upon the consumer behavior in a crisis. This enhances as well the discussion on

consumer behavior from the point of view of marketers and academics in the marketing field.

Furthermore, by applying a model decoupling consumer risk behavior, focus is shifted to the

reasoning of risk behavior and other psychological factors influencing consumer behavior, as

done in other literature (Pennings, et al., 2002).

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1.3. Research Question and Sub-Questions

Currently financial risks are in the spot light of governments and consumers. Apparently, the

reasons for the crisis do not seem clear. Economists were not able to forecast the crisis and

provide guidance through the crisis. However, with some hindsight economists agree that

excessive risk taking of market participants has caused the systems to fail and markets to

crash (Cowen, 2009). The popularity of deregulation especially in the financial sector

enhanced the trend of innovation. Yet, as innovation became faster and faster, regulators lost

track of what the players in the financial market did. Institutions invested in high risk products

unbalancing the system and leading to the collapse.

As consumer behavior tends to be irrational, especially in times of crisis, it is important to

define the psychological factors influencing behavior. An analysis like that explains the

behavior and shows how individuals respond to an economic crisis. By defining the emotions

and factors that are part of making decisions under risk, practitioners are able to avoid the

herding effect in later crises and restore trust by repairing relationships (Gounaris, et al.,

2009). As researchers found out that emotions such as fear and hope determine the decision

an individual makes under risk (Lopes, 1987), it is important to recognize this and adjust to

the behavior by reliable high-quality advising.

By relating consumer risk behavior and the consumption pattern by means of credit cards, as

well as psychological and emotional factors this study aims to understand consumers. Since

consumer behavior deviates from its “rational” behavior and perceived risk is considered in a

different way in times of crisis, the framework decouples risk behavior in two components as

mentioned above. This framework may help to develop strategies for crises and resolve these

without engaging in high losses and lots of bankruptcies, private as well as commercial.

Defining the psychological factors leads to predictable irrationality and an understanding to

resolve these problems.

In light of the current situation and individuals being annoyed and disappointed of the

reckless behavior from bank-advisors and practitioners the identification of drivers of

consumer behavior is ever more important. An effective communication needs to rebuild trust

and confidence in a renewed system.

For this reason the main problem statement underlying my research will be:

What drives credit card usage of consumers in times of crisis?

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Governments and private people have reacted to the economic crisis directly. Several

reductions of the prime rate, short time work, investments in gold, and lower consumption on

the consumer side pave the economic crisis so far. By discussing the regulations and

commending financial practitioners, governments try to develop strategies and relieve tension

on the market. However, these measures appear to be less suitable than governments assume.

More drastic and different measurements need to be designed and implemented to calm

consumers down and reduce the economic damage. As consumer behavior is not consistent

with the true level of risk in times of crises, strategists may be misled. Not being able to

forecast consumer reactions to the crisis and distinguish the factors influencing behavior

allows governments to take wrong measurements and impairing the crisis even further. In

order to effectively manage the crisis situation, a thorough understanding of what drivers

affect consumer behavior is essential to decision makers. This way signals towards either the

supply or demand side are more effectively communicated. Additionally, strategies are more

in line and consistent to solve the crisis with respect to both of these sides.

According to Taylor (1974) consumers face risk because of choices they make whereas the

outcome is uncertain. This risk leads to diverse consumer behavior depending on several other

factors. Emotional aspects of the individual and situational factors, such as a crisis, influence

consumer behavior and lead to the unpredictable outcomes in the respective situation. To

analyze and explain the inconsistency in consumer behavior, Pennings et al. (2002) decouple

risk behavior of individuals into risk perception and risk attitude. These two components are

considered separately as well as an interaction of both factors. Within the context of the

financial crisis I define the components as follows and in line with the definition of Pennings

et al. (2002):

Risk attitude (RA) reflects the predisposition to a particular risk in a consistent way that an

individual faces when making a decision under risk. It describes the content of the risk and

states how much a consumer likes or dislikes the risk that arises because of the financial

crisis.

Risk perception (RP) reflects the interpretation of the likelihood to be exposed to the content

of the risk an individual has when making decisions under risk. This component describes

how the individual assesses the uncertainty of the risk content in a particular situation. Hence

it defines the subjective probability that the risk through the financial crisis will influence the

consumer’s life (bad credit, insolvency) and reveals the individual’s awareness.

The interaction of RA and RP (RA*RP) analyzes the interaction of the individual’s attitude

towards risk and the subjective interpretation of being exposed to this risk. It shows that if a

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consumer is highly risk averse, and dislikes risk, she will attempt to reduce the chance of

being exposed to this risk.

The behavioral factor trust is harder to define, yet important as it influences consumer

behavior. With respect to financial issues many individuals rely on bank advisors and rules

and regulations from the government. In a crisis these points of reference are weakened.

Consumers feel that bank advisors and politicians are responsible for the crisis and the events

happening to them. Therefore, their trust in advisors and the system is no longer existent,

leading to panic and herding behavior. Experience with and information on the “hot topics” in

a crisis influence consumer trust as well. An experienced business man identifies the risk he is

exposed to and acts upon this. Furthermore, he will assess the information given by media and

government on a diligent basis and take decisions accordingly.

In light of the two components, risk perception and risk attitude, that I want to build the model

on, several additional research questions evolve that will be discussed with this paper. Hence,

the factor trust and its impact on consumer behavior will be analyzed and poses a fourth

research question:

Does risk perception drive the reduction of credit card transaction volume?

Does risk attitude drive the reduction of credit card transaction volume?

Does the interaction between both, risk attitude and risk perception drive the reduction of

credit card transaction volume?

Does trust consumers have in financial institutions and governmental actions drive the

reduction of credit card transaction volume?

As I will analyze several other factors that may drive the consumption behavior, subquestions

evolve concerning the demographics and socioeconomic factors. These factors will be

analyzed in a second step of the research.

Several streams of literature indicate that age as well as nationality influence the behavior of

consumers. The reason for this can be explained by the behavioral life cycle hypothesis

(Shefrin, et al., 1988). Moreover, Pennings et al. (2002) state that the cultural difference

between American citizens or Dutch citizens and Germans is quite high. Germans are less

trusting in information from their government and act more rational than the other nations.

Concerning gender, Barber and Odean (2001) find that females and males react differently

with respect to making investments. This indicates that consumption behavior may be

different as well, depending on the gender, and that women might be more careful when they

perceive a higher risk. Besides this, additional observable factors are education and income of

the individual. Depending on the degree of education, an individual is better able to identify

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the true level of risk and act upon this knowledge. Income is an interesting factor as it may

change an individual’s reference point and allow for higher or lower risk aversion.

These aspects indicate the importance to analyze the various factors influencing consumer

behavior in times of crisis and show that purchasing behavior by means of different payment

methods can be influenced through the crisis. The discussion above leads to the following

subquestions to be discussed in this thesis:

Why do consumers react differently to a crisis?

Do demographic and socioeconomic factors influence the reduction of credit card transaction

volume?

1.4. Outline

This thesis is set up in six chapters. The first chapter introduced the topic by giving a short

overview of the present situation and reactions to the economic crisis as well as introducing

the concept of risk behavior. In the second chapter I will proceed with an overview of existing

literature and past research upon behavioral finance, risk behavior, risk attitude, risk

perception, crisis events and socioeconomic influences. The third chapter will then explain the

conceptual model of my thesis, whereas the fourth chapter elaborates on the research design.

Chapter five composes the results section with data analysis and outcomes. In order to

conclude the thesis, chapter six presents the findings, as well as implications and limitations

of my study.

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2. Individual Behavior in Economic Crisis and the Credit Card Segment:

A selective Literature Review

2.1. Introduction

The research will combine the behavioral finance stream with the practical stream on financial

products. In order to review the various pieces written on this topic and contributing to my

research, I study the risk behavior of individuals as well as literature upon the topic of

payment methods, their usage and consumer attitudes. The resulting theoretical foundation

will then be employed to investigate individuals’ response to the economic crisis. Despite the

vast research upon consumer risk behavior from the marketing stream, I will focus upon the

financial context in the literature. Following closely the framework of Pennings et al. (2002)

and Kalogeras et al. (2010), this research deals with the decoupling of the risk response

behavior of consumers into the separate components of the risk perception and risk attitude.

Moreover, it will be analyzed what other demographic and socioeconomic factors influence

individuals’ risk behavior.

In the next section I will first review the different payment methods. In a second step, I will

review the basic behavioral finance topics applying to my research topic. Furthermore, I will

review the individual behavior as well as crisis events. Third an overview over the existing

academic work and concept of risk attitude and risk perception is given, whereas in a last step

various influencing variables will be reviewed, before concluding the section.

2.2. Payment methods

Individuals have the choice to pay by means of different techniques when purchasing products

and services. The most common payment method is paying cash. Besides this, consumers

may pay with check, bank transfer, or credit cards. I will concentrate on the credit card

payment only and elaborate on the mechanism behind this payment method. A credit card

allows an individual to pay cashless at the respective contracting partner. This implies that the

credit card holder is liable to the card issuer and holds a short-term credit. The card issuer can

be any bank, credit institution or credit card company. The issuer is then obligated to pay the

contracting partner and needs to claim the amount outstanding with the card holder.

There are four basic types of credit cards: Charge Cards, Credit Cards, Debit Cards and

Prepaid Cards. These credit cards differ according to the payroll accounting system. The

Charge Card is one of the most common card types in Germany. This system accumulates

turnover on a monthly basis on a separate card account. This account is then balanced once a

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month by the card holder. The system represents a short-term interest free credit, whereas the

credit line is set individually.

The Credit Card in its classical sense and usage in fact provides a credit to the individual. It is

one of the most common card types in the US and turnover is accumulated on a monthly basis

as well. The amount can then be paid in one lump sum directly, considered as non-revolving

credit cards and less common in the US, or in monthly installments at a later point in time,

considered as revolving credit cards. The installments are then charged with an interest and

the card issuer may require a minimum redemption directly.

The Debit Card functions in a different way than the credit card but its acceptance among

American citizens has risen dramatically. Using this card, turnover is directly withdrawn from

the account of the card holder, similar to the common checking account. Hence, the card

holder pays directly and does not take out a loan. Alternatively though, the debit card can be

employed on the basis of credit-side settlement. The account is balanced on a monthly basis in

that case, with the option to pay one lump sum or installments.

The Prepaid Card is an alternative to the classic credit card. The system requires the card

holder to load the card with credit prior to making purchases either through a cash transfer or

from the account. This credit is then charged with interest and can be used for purchases

(Grill, et al., 2003). The next section will present a general overview of behavioral finance

and the applicable theories to the topic at hand.

2.3. Behavioral finance: Risk Behavior of consumers, market anomalies and

crisis events

2.3.1. Behavioral Finance and Consumer Risk Behavior

According to Taylor (1974) consumers are exposed to risk because of the central problem:

choice. When making a choice, the consumer only knows the outcome after having made the

choice. Therefore, consumers are forced to deal with risk. Any risk associated with the

decision making process of a consumer has two aspects. The consumer faces uncertainty

about the outcome and uncertainty about the consequences of the outcome. This risk is

interpreted as a loss for the consumer and can be of psychological or economical value to the

individual (Taylor, 1974). Considering this discussion in light of financial theories, we arrive

at behavioral finance.

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In contrast to the traditional finance theories, behavioral finance assumes that investors are

not risk neutral. Considering the research at hand it means that with respect to finance,

consumers will not respond in a rational manner towards the current market movements and

economic conditions. When making decisions under risk the prospect theory by Kahneman

and Tversky (1979) applies. This theory opposes the expected utility theory in that individuals

are risk averse. Consumers faced with uncertainty will evaluate and then choose a specific

outcome with respect to a reference point. In this respect only gains and losses are considered.

Hence, individuals do not decide rationally but will be risk seeking in the domain of losses

and risk averse in the domain of gains. In addition, outcomes will not be evaluated according

to probabilities but according to subjective decision weights. This theory indicates that

individuals’ behavior deviates from rational decision making and depends on the respective

situation (Kahneman, et al., 1979). This behavior causes errors repeatedly and distorts the

market, which is specifically negative in light of crises.

Research in traditional finance claims, that markets tend to be inefficient, but these

inefficiencies are explained with market anomalies. The most popular ones being the calendar

effect (also seasonal effect: the effect represents the believe that at certain periods in a year

prices change above average and indicate investment opportunities, (Thaler, 1987)), the

dividend puzzle (the puzzle represents the fact that companies that pay dividends are

rewarded with higher valuations by investors, despite the fact that payment of dividends

should not matter to the firm value, (Myers, 1983)), and equity premium puzzle (the puzzle

represents the fact that investors in stocks earn abnormal higher returns than investors in

government bonds (Shefrin, 2002). However, we have behavior specific anomalies as well,

such as the disposition effect (this effect represents the behavior of investors to hold on to

investments in stocks losing in value and sell those stocks that are gaining in value, (Shefrin,

et al., 1985), momentum investing (this term refers to the investment strategy of market

participants to buy stocks with high returns over the last period of three to twelve month and

sell those with low returns over the last period in order to make a return of up to 1%,

(Jegadeesh, et al., 1993) and herding behavior (the tendency for individuals to behave as a

herd in an economic turmoil and rush in or out of the market, (Chiang, et al., 2010).

Discussing whether markets are efficient or inefficient, is beyond this research. Yet, I want to

emphasize that traditional finance may not explain the inefficiencies accurately and

behavioral finance can contribute to the resolution of some anomalies with respect to decision

making under risk.

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As mentioned above individuals behave rather irrational and behavioral finance tries to

explain this behavior through behavioral anomalies, heuristics and framing. The major frame

dependence to consider in context of my research is loss aversion. Individuals are loss averse

and cannot come to terms with the losses they make. Kahneman and Tversky (1979) explain

loss aversion in context of the prospect theory. According to the theory, people weigh losses

heavier than gains and suffer from “get-evenitis”. In academic literature this phenomenon is

called the disposition effect. It accounts for the disposition of individuals to sell “winners” too

early and ride “losers” too long. This means that investors are prone to loss aversion and hold

on to losing stocks while they fear a winning stock to lose value in the near future and sell

those. In their article Shefrin and Statman (1985) develop a model that incorporates the

behavioral elements of prospect theory, mental accounting, regret aversion and self-control to

explain the disposition effect and decision-making under risk.

The prospect theory induces individuals to act risk seeking in the domain of losses and risk

averse in the domain of gains. For this reason, individuals hope to gain with losing

investments in the future and hold on to them. Mental accounting will be analyzed further

below and describes the segregation of different types of investments into separate mental

accounts. Due to “get-evenitis” and loss aversion individuals avoid closing accounts at a loss,

which could be circumvented by transferring the assets. This action prevents the individual

from closing an account at a loss and hence avoids the realization. Moreover, as individuals

seek pride and avoid regret, the disposition effect appears to be a natural consequence. Similar

to the risk behavior under the prospect theory, individuals will prefer to realize gains and

enhance their pride while they will avoid realizing losses in order to circumvent regret for the

decision. The fourth element, self-control, approaches the intrapersonal conflict an individual

faces in the context of decision making under risk. This conflict appears between the rational

part, the planner or principal who has willpower, of an individual and the emotional myopic

part, the doer or agent representing pride and regret. Emotions influence the decisions made,

wherefore the individual needs to restrict herself by taking measures of self-control. These

measures force loss realization via stop-loss-orders for example (Shefrin, et al., 1985).

This discussion indicates that emotions influence individuals when making decisions. In

greater detail emotions influence individuals’ tolerance for risk as Lopes (1987) explains.

Hence, this tolerance for risk influences consumer credit behavior in times of crisis. Several

emotions determine the tolerance for risk: fear, hope and goals directing towards aspirations.

Hence, markets are not solely driven through the emotions greed and fear. There are more

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emotions that influence an individual’s behavior and go beyond those two. The two emotions

fear and hope induce individuals to focus on either unfavorable or favorable events,

respectively. As a third pillar individuals focus on goals they aspire, for example retirement or

children’s education, relating risk tolerance to the behavioral life cycle hypothesis (BLC).

This hypothesis seeks to explain how people chose among consumption and saving over the

course of their life cycle with behavioral factors and represents one anomaly contrasting

efficient markets. The BLC hypothesis may shed light on my research topic and explain part

of the reason to include behavioral aspects into academic studies. Based upon the rational

microeconomic concept from Modigliani and Brumberg (1954), Shefrin and Statman (1985)

see their research as an extension to explain the irrational behavior of consumers over their

life time. Therefore, in addition to the general finding that consumption is not based upon

current income but depends on a constant percentage of the present value of the individuals’

life income the BLC includes the behavioral factors of self control, mental accounting and

framing (Shefrin, et al., 1988).

Self control has been discussed above with respect to the prospect theory, for which reason I

will focus on framing in this section. Framing addresses the fact of how individuals organize

their mental accounts in a decision making context. Hence, it describes cognitive as well as

emotional factors and offers a distinction between their form and substance. Individuals will

frame their personal accounts according to income and wealth, whereas the wealth accounts

are tempting for riskier investments. When comparing the traditional theory to the behavioral

theory, the BLC offers an explanation for the irrational behavior of individuals. Moreover, in

their article Shefrin and Thaler (1988) find that with respect to saving over the life cycle,

behavior cannot be explained with one single utility function and that other factors than age

and wealth, such as payment method of income may affect the consumption. This appears to

be of interest, as the life cycle seems to influence risk tolerance and that this tolerance is

affected by several factors including emotions (Shefrin, et al., 1988).

Regarding the emotions fear and hope, and aspirations, the emotional time line explains how

the life cycle relates to emotional factors and how these affect risk tolerance. On the

emotional time line time moves ahead from left to right and investment decisions lie at the

left, whereas goals and thus aspirations lie on the right side of the line. Along this time line

individuals experience diverse emotions, from making a decision, waiting and finally

realizing the outcome. Placing positive emotions above the time line and negative emotions

below the time line, the relation between the process of making decisions and emotions

19

becomes clear. Above the time line hope is transformed into anticipation and at a later point in

time into pride, whereas below the time line fear is transformed into anxiety first and then into

regret.

As individuals evaluate their alternatives, hope and fear play a vital role. From a bottom-up

perspective, the emotion fear causes an individual to consider the worst case scenario and

addresses the desire for security. In contrast the top-down perspective, based on the emotion

hope, addresses the upside potential and hence a best case scenario. The tolerance for risk

according to Lopes (1987) is determined by these two conflicting emotions. Every individual

is subject to the two perspectives mentioned above, however these are not equally matched.

For that reason individuals react in different ways when making decisions under risk,

depending on whether fear or hope is the dominant emotion (Shefrin, 2002).

Hence, investors select their portfolios in form of a layered pyramid. These pyramids

represent safe securities at the bottom and highly speculative investments at the top. By

planning their financial situation investors “apply hope” and decide how much of their

financial recourses to devote to each layer of the pyramid. Fear and hope will determine the

tolerance for risk an investor is willing to accept, depending on the aspiration level they set

for themselves (Shefrin, 2002). This discussion underlines the importance of emotions in the

decision making process under risk and shows that individuals cannot be considered to make

truly rational choices.

The work of Prelec and Loewenstein (1998) addresses the behavioral and emotional aspects

of consumer decisions compared to standard economic theories as well. In the standard

economic account of consumer behavior, the present value of payments is minimized to

derive the greatest utility. However, considering hedonics consumers feel a “pain of paying”.

In order to conceptualize this behavior, the authors suggest a double entry mental accounting

theory. This theory describes how the reciprocal relationship between the pleasure of

consumption and the pain of paying interfere and the impact they have on consumer behavior

and hedonics. Consumers create mental accounts to distinguish and affiliate consumption and

payment. In these accounts consumers are able to balance their expenditures and utility from

consumption. Although traditional theory suggests that immediate consumption and deferred

payment is preferred, the model at hand depicts strong debt aversion and a preference to

prepay. This underlines the central assumption of the double entry mental accounting theory,

the prospective accounting. In this case consumers consider consumption that has been paid

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beforehand as free and “buffer” the pain of paying by thinking of the benefits the

consumption will bring, such as for a vacation paid in advance.

The theory is based upon two sets of entries. One captures the net utility from consumption

after subtracting the disutility from the “pain of paying”, whereas the second one captures the

net disutility after the utility from consumption has been subtracted. As a consequence

consumers face imputed costs attenuating the consumption and imputed benefits buffering the

disutility derived from payment. These costs and benefits, however, depend on the timing of

consumption and payment as well as the method of payment. A second assumption in the

theory is the prorating assumption. In this case consumers spread a single payment over

several acts of consumption or a single utility over several payments. The last assumption,

coupling, implies that consumers impute the costs and benefits of consumption imperfectly.

Regarding debt aversion consumers evaluate acts of consumption and the payment depending

on whether one account is in the red or the black numbers. This implies consumers desire to

keep accounts in the black and rather pay off the act of consumption before the utility has

dissolved. Due to this phenomenon it is no surprise that debit cards are preferred over credit

cards and became popular. The main advantage for consumers in this case is the fact of not

being indebted through an act of consumption. Another behavior describing the preference for

prepayment is the mental prepayment. Consumers set aside money for future consumption.

However, this is not only done to exert self-control but enhances the hedonic benefits of

consumption. Fixed fees exert a similar utility for consumers, by eliminating the uncertainty

of the payment amount. Considering the payment devices an interesting aspect is the

interference of credit card payment and the third assumption, coupling (Prelec, et al., 1998).

In addition to the recent developments in consumer behavior towards credit card transaction

volume, some researchers have found further factors influencing consumer behavior. Zinman

(2009) finds an increase in debit card usage and a decrease in credit card transaction volume.

His results show that consumers holding revolving credit cards are more likely to use their

debit card as well as consumers facing higher costs on their marginal credit card charges.

Furthermore, as credit limit constraints are stricter, consumers prefer debit cards over credit

cards. Nevertheless, the possession of a credit card reduces the probability that consumers pay

by means of debit cards. By considering the four margins of acceptance: security, time costs

and probability and controlling for demographics, financial attitudes, income and ownership

of an ATM card, Zinman (2009) concludes that consumers do not choose payment methods

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randomly but on a rational basis. Yet, he cannot reject models based on mental accounting,

linking us back to behavioral finance theories (Zinman, 2009).

In order to advance the understanding of the various building blocks of this research, I will

now review crisis events and link these themes in the concluding section.

2.3.2. Crisis events

Why did the current financial crisis occur and how do individuals respond to crisis events?

This research aims to increase the understanding of the role of risk attitude, risk perception

and trust of the individual decision making behavior in times of crisis. Therefore, I analyze

this crisis event and its effect on consumer behavior before developing the conceptual model.

According to Malmendier and Nagel (2009) experiences of macro-economic shocks have

long-term effects on risk attitudes of individuals. Specifically, they find that individuals, who

have experienced stock markets with high returns throughout their life, encompass lower risk

aversion. Individuals, who experienced high inflation, are more risk averse. Those will avoid

investing their wealth in bonds and prefer “inflation-proof cash like” investments. Although,

most recent events have had the strongest effect on risk attitude, experiences in the early life

of an individual influence risk behavior as well and this impact fades only slowly. This

analysis supports the view that economic events, an individual experiences, impact risk

behavior and beliefs more than historical facts learned from information in books or other

sources. Hence, investor behavior depends on the experienced events and is subjective.

Wherefore it cannot be objectively determined through publicly available information and

applied in rational models (Malmendier, et al., 2009).

The current economic crisis will certainly influence individuals’ behavior and the future with

respect to the financial system and its regulations. However, if the crisis is this obvious in

hindsight, why did politicians and economists not identify the crisis earlier? Schneider and

Kirchgässner (2009) define three main problems to have caused the current crisis. First of all,

financial innovation and highly complex products line the way. In addition, deregulation of

capital markets and excessive spending by consumers lead towards cheap credit and easily

accessible money on an opportunistic market. By using models based upon rational

individuals and stable markets for predictive purposes, unrealistic scenarios were developed.

In volatile and unregulated markets rational models are not reliable. For this reason, the

financial sector developed more and more upon a bubble of high risk investments and

speculative instruments. In contrast, these type of investment tactics were recommended by

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highly reputable analysts and managers (Madoff scandal: Madoff used the Ponzi scheme for

one of the largest investor frauds until today and represented one of the main market makers

in the financial market; He operated an investment fund on the basis of the Ponzi scheme and

caused a damage of over 50$ billion to around 4800 individuals). Individuals trusting in this

system followed recommendations and wanted to profit from “cheap money” (Schneider, et

al., 2009). Therefore, an unstable system cracked and finally collapsed in a world economic

recession. To learn from these errors, the system needs to change. Moreover, new regulations

need to be installed and people need to understand that investments yielding high returns are

of higher risk. Many individuals lost pensions and other savings, hoping to earn money as

they did not consider the risk behind these investments. Thus, the future may change due to a

different risk behavior of individuals especially with regard to financial matters.

As mentioned before, the economic crisis spread over the whole world and demonstrated that

the financial system is globally linked. Therefore, it is difficult for governments and managers

to induce the correct measures. Decision makers are not able to properly forecast individuals’

behavior in a crisis situation, particularly at the infant phase of a global crisis. As individuals

are subject to framing and herding effects, it is of importance to respond in the most

appropriate way and limit their loss from such a crisis situation. Pennings and Grossman

(2008) suggest encountering this problem with a framework of two dimensions. Defining risk

attitude and risk perception based on Arrows and Pratt’s framework it is possible to quantify

risk behavior. As an extension they introduce the behavioral outcome space (BOS). The BOS

denotes the behavior of all citizens and by minimizing the BOS, it limits the uncertainty in a

crisis situation. Hence, uncertainty is converted into risk, for which reason decision makers

can assess the behavior of individuals and apply appropriate measures. The analysis then

allows determining factors influencing and driving risk behavior, revealing important

indications in a crisis situation and possibly limiting damage (Pennings, et al., 2008). In case

of a financial crisis this aspect is essential as well and the components will be further

reviewed in the following section.

2.3.3. Credit Cards and Consumer behavior

Two main risks raise concern with regard to credit card usage. First of all consumers may not

fully understand implications and costs credit cards render. Furthermore, the risk of

overindebtedness or financial distress due to bankruptcy and bad credit pose risks for

consumers. Based upon this, consumers develop a general attitude towards credit cards and

23

adjust their usage behavior. The two main purposes for consumers to use credit cards are

substitute for cash payment and a source of revolving credit. Hence, almost every household

possesses a credit card and uses these for some purchases. The general attitude by consumers

on whether credit cards are good or bad were found to be more negative than positive in the

year 2000 (Durkin, 2000). However, credit card owners viewed credit cards more positive

than the general population. Additionally, the survey showed that consumers perceive credit

cards in a different way depending on the personal use and experience with the payment

method. Thus consumers possessing more than three cards and with outstanding balances,

consider credit cards less positively than those consumers with less than three credit cards,

being in balance on their accounts. The generally worsened attitude towards credit cards may

steam from the fact that consumers feel confused about some practices. The general opinion

of consumers towards others use of credit cards appears to be very negative. Consumers fear

that too much credit is available and that practices in the credit card market are somewhat

negligent (Durkin, 2000). Nevertheless consumers feel positive about their personal usage of

credit cards, but would appreciate more information about credit terms, costs and the time to

pay off credit. Although these attitudes depend on the demographic and socioeconomic

situation, the article indicates that consumers are aware of risks and uncertainties within the

credit card market. Despite this knowledge, consumers are willing to use credit cards and

make purchases on credit. I expect this behavior to have changed in light of the financial crisis

and the turmoil in the banking system due to the credit crunch.

An interesting study on usage behavior, relating to the findings of Durkin (2000), defines four

different types of credit card attitudes that consumers are subject to. Consumers who have a

“credit card averse” or “credit card felt-involved” attitude purchase less by means of credit

card. In contrast consumers subject to “credit card prone” attitudes and “outer-directed credit

card” attitudes have the tendency to charge their credit cards as much as possible (Kara, et al.,

1996). Therefore, depending on the attitude consumers behave differently when making

purchases and practitioners may consider this when offering services and products. Yet, other

factors such as demographics and socioeconomic aspects as well as experience with credit

cards influence usage behavior. This means that consumers view credit cards either as bad and

consider them fairly risky, wanting to repay all debt at the end of the month, or they consider

credit cards as good, providing services otherwise not available (car rental, hotel etc), and do

not worry about debt burdens. It appears that consumers perceive some kind of risk, but also

constitute a risk as they behave improper due to misguidance, which can be changed by

providing better information and services.

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Despite the risk of not being able to pay back the debt of purchases on credit card, consumers

form positive attitudes about credit cards. This is mainly due to ownership and experience,

where consumers consider credit cards as very popular and easy to use. The information

individuals collect on credit cards, are mainly provided through the banks, where consumers

have their personal bank account. In addition consumers collect information from friends and

family and to a lesser extend from flyers and commercials. However, consumers consider it

strenuous to apply and purchase a credit card in the first place. One might expect this view to

change in light of the crisis. Furthermore, as consumers were and are aware of costs of credit

cards, it can be expected that the crisis changes attitudes to the perception of these costs and

perceptions on companies.

Overall it can be seen that consumers have always considered that there is the risk of

overindebtedness. Yet, credit cards are used and usage behavior may not entirely comply with

the attitudes towards credit cards, when considering the awareness of costs and information

grievances. In light of the crisis this behavior may have changed towards a more cautious and

anticipatory behavior, which will be analyzed within this study.

2.4. Risk Attitude

The following section will first of all discuss risk attitude in general after which I will review

literature upon risk attitude of consumers and risk attitude of individuals within the financial

context. Thus, this section seeks to introduce the basic theoretical pillars of the conceptual

model for a better understanding of the research.

2.4.1. Risk Attitude in general

Risk attitude poses one dimension in the context of making decisions under risk. The

definition that Pennings and Wansink (2004) and Pennings et al. (2002) give for risk attitude,

implies that consumers have a general predisposition to risk in a consistent way. Hence, when

making a decision the consumer interprets the content of the risk she faces and expresses how

much she likes or dislikes this risk. This risk attitude ranges from being risk seeking to being

extremely risk averse. In order to analyze the relationship between risk attitude, risk

perception and the interaction of both, Pennings et al. (2002) base their framework upon the

work of Pratt (1964) and Arrow (1971).

In their work the two researchers develop an equation stating the relationship between risk

attitude, risk perception and the interaction of both. Therefore, they define risk management

to be reflected in a risk premium 𝜋. This risk premium is a function of risk attitude, where r is

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the risk aversion, the specific base situation, where W is the base wealth, and risk perception,

with a mean of 𝜀 and 𝜎2, a variance of source of additional wealth ε. An individual managing

risk is then considered to be indifferent between the exposure to risk, being involved in the

risky situation, or the mean value minus the risk premium. Expressing this relationship in

form of the expected utility model reveals the following equation:

𝐸𝑈 𝑊 + 𝜀 = 𝑈(𝑊 𝜀𝑓 𝜀 𝑑𝜀 − 𝜋)

with 𝑈 ∙ being the Neumann Morgenstern utility

and 𝑓 ∙ the probability density function of additional wealth 𝜀

Rewriting this formula, it is shown that by introducing the Pratt-Arrow coefficient of absolute

risk aversion 𝑟 𝑊 = −𝑈"(𝑊)/𝑈′(𝑊) the risk premium equals 𝜋 =1

2𝜎2𝑟(𝑊). Hence, Pratt

and Arrow reveal that risk management behavior of individuals is dependent on the three

components risk attitude, risk perception and the product between them. This decoupling

offers us to arrive at better strategies and make better predictions with respect to consumer

behavior and individuals’ decision making under risk. For this reason I will apply a similar

framework in my conceptual model. However, there are different ways researchers defined

risk attitude and measurements for this component that I will shortly review as well.

The extension to the above mentioned expected utility framework by Pratt and Arrow is given

by Kahneman and Tversky (1979). With regard to the above discussion of behavioral finance

theories and in particular the prospect theory, a slightly different definition of risk attitude is

provided. Under this definition an outcome from making a decision under risk is assessed

relative to some reference point of the individual. Yet, risk attitude is based upon a value

function, under which individuals act risk seeking in the domain of losses and risk averse in

the domain of gains. Important to notice here is the definition of making decisions under risk,

not uncertainty.

In a further extension Kahneman and Wakker (1995) introduce the prospect theory including

the weighting function to decision making under uncertainty. They specifically define

uncertain events as sports events or the weather and risk as the risk individuals face in certain

situations. Hence risk attitude is essentially converted into “uncertainty attitude”. The main

difference in this definition is that people consider uncertain events in light of possibility and

certainty, whereas risk is considered in light of probabilities. In traditional prospect theory the

26

weighting function is incorporated to explain decision making under risk, and specifically the

s-shaped curvature of the function due to certainty and “lottery”. Kahneman and Wakker use

this weighting function in the context of uncertainty to explain that an event has a greater

impact on the individual whenever impossibility is turned into possibility or possibility into

certainty. This is in contrast to the traditional prospect theory where the specific event is

simply more or less likely to happen.

Another definition for the concept of risk attitude is provided by Dyer and Sarin (1982). In

their study the authors propose two factors that influence an individual’s preference for risky

alternatives. On the one side there is the strength of preference an individual feels for the

specific consequences. Whereas on the other side there is the individual’s attitude towards

risk taking. Therefore, Dyer and Sarin (1982) propose the relative risk measure to separate

marginal value for outcomes from attitudes towards uncertainty. As these two factors were

confounded in the conventional expected utility framework, the work of Dyer and Sarin

(1982) attempts to enhance research in this field. The new relative risk measure enhances the

understanding of an individual’s attitude towards risk. Moreover, it improves the way an

individual can combine preferences of several experts in the context of multi-criteria decision

making. It can provide a better understanding of the implications that commonly used

preference aggregation rules in group decision making offer. Thus, relative risk attitude

implies that individuals have a constant relative risk attitude, whereas the measurable value

function around the status quo point follows the s-shaped curvature as developed by

Kahneman and Tversky (1979). A constant relative risk attitude indicates a stable personality

trait of the individual. More concretely this means that individuals may consider risk

introduced by a lottery with decreasing marginal value. The strength of preference for a

specific alternative defines the most valuable outcome. Hence, in case an alternative becomes

slightly better by chance of lottery an individual’s marginal value of this alternative indicates

a decreasing function. For this reason Dyer and Sarin (1982) denote the individual as

relatively risk neutral in that specific situation. By introducing this new concept the authors

expand the research in the risk attitude field and offer a different definition. Nevertheless,

their definition may have been unsuccessful to define a more stable approach of measuring

risk attitude (Weber, et al., 1997).

Smidt (1997) analyzes risk behavior within the same framework as Dyer and Sarin (1982). He

specifies intrinsic risk attitude as the relationship between two distinct factors, risk attitude

according to the utility function and the strength of preference according to the value function.

27

By studying this concept Smidt (1997) attempts to find the relevance of the intrinsic risk

attitude concept and whether there is a relationship between the expected utility function and

the value function. His finding confirms the hypothesis, that risk attitude and the strength of

preference are two distinct constructs. Moreover, the relationship is not linear but exponential

and individuals show diverse risk behavior across various situations. Overall, this study shows

that in order to assess individual risk behavior one should distinguish between choice under

uncertainty and under risk or more precisely between risk attitude and the strength of

preference (Smidts, 1997).

Weber and Bottom (1989) suggest perceived risk attitude as a definition. Under this theory,

the authors relate risk perception to choice behavior, whereas risk preference is defined as the

preference for the alternative an individual perceives as least risky. Using statistical tests the

authors were able to proof that perceived risk attitude has significant cross-situational stability

when analyzing risk behavior. Therefore, the consistent results of the Weber-Bottom study

imply, that individual’s perceived risk attitudes are reliable towards the risk averse or risk

seeking behavior for lottery choices.

Weber and Milliman (1997) apply this definition of perceived risk attitude and test it against

the relative risk attitude approach of Dyer and Sarin (1982) as well as the traditional expected

utility framework. In their study they find that the operationalization of risk preference is

more stable in specific situations than the other measurements mentioned above. The

operationalization of risk preference factors the differences in risk perception out of the risky

choice that consumers have. Therefore, they presume risk preference to be a stable personality

trait and changes in risk perception may affect situational variables. In this context, as

described by Weber and Bottom (1989), risk preference entails risk perception. Hence, risk

preference depends on the attraction or repellence of any alternative that is perceived to be

risky by an individual.

2.4.2. Findings on Risk Attitude

After assessing risk attitude in general and giving insight on the research status of the risk

attitude construct, I will attempt to review some selected findings upon this topic.

The work of Weber and Hsee (1998) examines whether individuals in four different countries

are subject to dissimilar risk preferences. The study is conducted in Poland, Germany, China

and the U.S. and analyzes in how far similar or dissimilar risk preferences depend upon risk

attitude, risk perception or on both of them. Therefore, the authors apply a framework

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distinguishing between risk perception and the perceived risk attitude resembling an

individual’s risk preference. With this model the authors found that perceived risk attitude is

principally similar across cultures, which means that individuals are generally risk averse. The

different risk preferences are based on cultural differences in the perception of risk, which can

be ascribed to the different market structures in the various countries and the state of

economy. For this reason Germans and Americans show similar risk preferences, as the

capitalistic market structure appears to be rather stable. Chinese and Polish consumers appear

to show similar risk preferences as well considering the change from planned to market

economy at the time of the study. Overall, these results may give an indication to consumer

risk behavior considering the cultural backgrounds.

The study of Pennings et al. (2002) deals with individuals’ risk behavior in times of a crisis

concerning food products, the mad cow disease. This research decouples risk response

behavior into risk attitude, risk perception and the interaction of both and examines consumer

behavior across the three nations: Germany, Netherlands and U.S. Within in this framework

Pennings et al. (1992) find the drivers for individual risk behavior. They find that the relative

impact of risk attitude and risk perception on risk behavior depends on the individual’s

accuracy of knowing the probability to be exposed to the specific risk. Overall, the findings

show that German consumers are significantly more risk averse than the other two nations

with respect to beef consumption. These findings are in line with prior findings, stating that

Americans and Dutch belong to a similar segment when comparing the nations on Hofstede’s

uncertainty avoidance index. This result may be based on the trust Germans, Dutch and

Americans put in their government. Despite the risk perception, Germans are afraid of the risk

related to the contamination with BSE and this depends as much on risk attitude as it does on

risk perception. Hence, in Germany an elimination of risk is as effective as better

communication, and both means have to be applied to change risk behavior in times of crises.

Generally the authors conclude that strategies employed to antagonize a crisis, will only be

effective if the true reason behind the risk behavior is identified and considered.

By applying the same framework Pennings and Wansink (2004) investigate channel contract

behavior in light of risk influences. Their research yields that risk attitudes vary significantly

across different levels of channel members. Moreover, the interaction of risk attitude and risk

perception (IRAP) constitutes a strong predictor of contract behavior if combined with the

channel member’s market structure on the buying and selling side. In addition to this the

impact of IRAP on channel contract behavior strengthens when channel power increases.

29

These results imply that IRAP is important when trying to predict channel behavior in markets

and direct towards the main factors one has to consider in this.

The article “A Domain Specific Risk-attitude Scale: Measuring Risk Perceptions and Risk

Behaviors” by Weber et al. (2002) considers decision making under risk in a similar way to

Weber and Hsee (1998). Therefore, the authors analyze risk perception and the attitude

towards perceived risk, risk averse or risk seeking, in five different content domains: financial

decisions, health/safety, recreational, ethical, and social decisions. The study yields that

individuals’ risk behavior is highly domain specific. This implies that individuals are neither

risk averse across all domains nor risk seeking. Thus, risk taking depends on situational

characteristics, but as the authors found out as well on personal traits and gender. Another

challenging finding is that perceived risk attitude depends merely on the specific benefits or

losses a choice under risk may offer. Therefore, the individual may be less risk averse in the

financial content domain in case the profit is higher. Additionally, risk behavior for financial

decisions varies depending on the form: gambling/chance, investment, personal or business.

This may indicate that risk behavior in the financial context in a crisis is even harder to

predict than risk behavior towards health/safety and recreational decisions.

A study examining risk behavior in light of a financial context was done by Pennings and

Garcia (2004). This study analyzes the hedging behavior in small and medium-sized

enterprises (SMEs). However, the factors influencing derivative usage are not equal but

heterogeneous. Hence, risk exposure, risk perception, risk attitude and the decision-making

unit among others do explain the hedging behavior of SMEs to varying degrees. An

interesting finding shows that managers are influenced by their decision making unit when

deciding on the derivative usage. Although managers eventually decide the derivative usage

on their own, this outcome indicates that financial institutions should consider the decision

making unit as a whole in their marketing efforts. In addition firm size as well as risk

exposure influence the derivative behavior of managers. In a second step the authors made a

latent segmentation. This segmentation showed that managers behave heterogeneous across

segments with regard to manager and firm characteristics and derivative usage. The first

segment exhibits a significant association of derivative behavior with risk exposure, size of

the firm, influence of the decision-making unit, the manager’s risk perception and IRAP.

Compared to segment two and three this segment displays the least derivative usage. Segment

two exhibits that risk exposure, size of the firm and the level of education are associated with

a modest derivative usage and have a significant effect. Lastly, segment three shows the

30

highest derivative usage. This usage is associated significantly with the three main

components of the conceptual model, risk attitude, risk perception and IRAP. Therefore, usage

behavior can be interpreted easily and the role of risk attitude becomes clearer. Risk averse

individuals will employ more derivatives than risk seeking managers. Despite this logical

behavior, leverage, the level of education and the decision making unit affect derivative usage

as well in this segment, which appears to be the smallest of all. Overall, the study reveals that

risk behavior is driven by fundamental factors (RA, RP and IRAP) when considering segment

three. Nevertheless, smaller firms (segment one) adjust the behavior according to risk

exposure and the environment (Pennings, et al., 2004).

2.5. Risk Perception

In order to arrive at a coherent model the next section will review risk perception in general.

In a second step I will elaborate on existing literature on risk perception and risk perception in

the context of finance, especially the credit card field.

2.5.1. Risk Perception in general

Risk perception constitutes the second dimension in making decisions under risk. According

to Pennings and Wansink (2004) and Pennings et al. (2002) risk perception exhibits an

individual’s interpretation of the likelihood to be exposed to the content of the specific risk. In

this research risk perception denotes the chance of an individual to be exposed to the risk of

default when using credit cards. For example an employee at a distressed company may have

interpreted the chance to be exposed to default risks concerning credit cards very high (90%-

100%), since there was a certain risk to become unemployed. As before risk perception is

applied within the framework by Pennings et al. (2002) based on Pratt (1964) and Arrows

(1971) work.

Furthermore, it is important to note that risk perception has to be distinguished from perceived

risk in this framework. In order to truly decouple risk behavior into the three components,

Pennings et al. (2002) consider risk perception only with respect to the uncertainty

component. The perceived risk term in contrast takes two dimensions into account. Hence,

perceived risk comprises the uncertainty component as well as the seriousness of adverse

consequences (focus on potential negative outcomes). For the research at hand it is important

to define risk perception in the above mentioned way, including only the uncertainty

component (Pennings, et al., 2002).

31

In the formulas displaying the constitution of risk management behavior in the section above,

risk perception was one of the variables besides risk attitude. Thus, Pennings et al. (2002)

consider risk perception in context of the expected utility model. However, other

interpretations of risk perception do exist and will be reviewed succinct.

The study “Factors in Risk Perception” by Sjöberg (2000) asserts that many models on risk

perception fail and are able to explain a small fraction of risk perception at best. He therefore,

develops a model that strives to explain more of risk perception itself and includes more

factors. According to Sjöberg (2000), the psychometric model on risk perception appears to

be cognitive in its conception and flavor and is thus insufficient. Risk perception is illustrated

as a function of properties of the hazards, it is truly perceived. However, this is insufficient to

truly represent risk perception even in case real risk (technically measured) is considered. In a

similar way the cultural model does only consider one factor, although in a very different

context. By viewing risk perception as a reflection of the social context an individual is in,

this model is insufficient as well. Sjöberg (2000) claims that it cannot be used only one factor

to express risk perception. For that reason he explains risk perception by means of risk

attitude, as the first crucial factor, risk sensitivity, and specific fear. These three factors

indicate that risk perception is an expression of different specific values and relates to the

personal and scientific psychology in a natural way.

Pablo and Sitkin (1992) focus on risk perception and risk propensity in relation to risk

behavior, regarding them as more central to risk behavior, than was previously done in

research. The findings suggest that risk propensity is the dominating component in the model

on risk behavior and influences the actual and perceived risk characteristics of individuals.

However, I will focus on the definition of risk perception in this section. Risk perception is

described as being the “individual’s assessment of the risk inherent in the situation” (Pablo, et

al., 1996). The authors identify five variables that have direct effects on risk perception: top-

management team homogeneity, problem framing, and organizational control systems, the

social influences of the organization’s cultural risk values and leader risk orientation, as well

as problem domain familiarity. A sixth factor affecting risk perception is risk propensity. It is

the second component of the model indicating an individual’s tendency to generally take or

avoid risk. Therefore, an individual will perceive more risk in case she is subject to a risk

seeking propensity and vice versa.

Top-Management team homogeneity does affect risk perception. In homogeneous teams,

individuals will reveal risk perception in similar or extreme ways. Moreover, such teams will

32

be certain of the accuracy of these perceptions. Problem framing plays a role, as positively

framed events will be perceived to be more risky than negatively framed events when seen in

relation to a normative risk scale. This finding is in line with the prospect theory discussed in

a previous section of this chapter. Furthermore, organizational control systems do influence

risk perceptions according to their reward and punishment structure. In case focus is placed on

process control risk perception is lower. However, this is not the case if the focus is on

outcome controls. Social influences of the organization’s cultural risk values and leader risk

orientation influences individuals risk perception as the individual will perceive risks more

slowly and accurately in an organization with moderate cultural risk values. Additionally, an

individual will adjust the personal risk perception towards the leaders risk values in an

organization. The last factor influencing risk perception is the problem domain familiarity.

This factor reflects an individual’s experience in a specific situation. Hence, perceived risk

depends on past experiences of the individual and influences her risk perception. Overall the

study depicts that despite the dominant influence of risk propensity on risk behavior, risk

perception is important in determining individual risk behavior as well. As an important

explanatory variable risk perception is able to explain observed variations in individual risk

behavior, not yet clarified by risk propensity (Sitkin, et al., 1992).

2.5.2. Findings on Risk Perception

I will now proceed with a review of selected research and findings on risk perception, after I

have presented the general concepts and definitions of risk perception in the section above. In

the section on risk attitude one could already see a strong relation between risk attitude and

risk perception and some of the studies mentioned above certainly add valuable findings to

this section as well.

Mitchell and Greatorex (1993) research risk perception in the context of consumer services

among students. The authors assert that services compared to goods confront consumers with

higher risk due to the uncertain outcome. Hence, services are perceived to be risky and among

the most risky services in the study at hand was the hairdresser, hotel and the bank.

Interestingly the authors defined what risk consumers are facing in these services. Therefore,

consumers feared a financial loss the most. This is however not the case for the hairdressing

service, as consumers perceived a psychosocial risk in this service as being most severe. In

addition consumers formulated to perceive risk for a time loss and a physical loss when

purchasing services (Mitchell, et al., 1993).

33

Pennings et al. (2002) address the issue of risk perception as well. As discussed above, the

authors apply the Pratt-Arrow framework to find out what drives risk behavior of consumers

in times of crisis. They find that risk perception, as the chance to be exposed to the specific

risk, is higher in Germany than in the U.S. and the Netherlands. This finding shows that risk

behavior is different among cultures and not independent. Furthermore it is interesting to see,

that risk behavior of the Dutch is mainly driven by risk perception, whereas risk behavior of

Germans is driven by risk attitude and risk perception together. Hence, the Dutch government

can attempt to communicate consistently and effectively what measures are taken to reduce

the risk and protect consumers. In a second step of this study Pennings et al. (2002) analyze

whether consumer behavior changes if the probability of being contaminated with BSE

changes. The findings are interesting. The influence of risk attitude on beef consumption

changes, except for German consumers. Furthermore, risk perception does not rise when the

chance for contamination rises and does not influence beef consumption. Again this indicates

that correct measurements and effective communication of governments reduce the extreme

behavior by consumers (Pennings, et al., 2002).

Pennings and Garcia (2004) apply the same framework to research hedging behavior in

SMEs. As the study was explained in more detail above I will solely focus on risk perception

in this part. Risk perception is measured based on a scale consisting of a number of statements

that measure the extent to which industry members perceive a market as risky (multi-indicator

measurement). As risk attitude and risk perception are not individually significant, it is

surprising to find the IRAP to be significant for hedging behavior. Apparently, risk attitude

relates risk perception to behavior, which is indicated through IRAP. In the second part of the

study, where the authors conduct a latent segmentation, risk perception appears to be

significant in segment 1. This segment is dominated by small firms, not using hedging

techniques and making usage dependent on risk exposure and opinions of the decision making

unit. In contrast segment two, with modest derivative usage appears to be not determined by

risk perception at all. Nevertheless, segment three is denoted by significant impact of risk

perception, risk attitude and IRAP. This indicates that risk perception appears to be an

important concept when deciding on the derivative usage, as a risk averse manager who is

subject to high risk perception will use derivatives more heavily (Pennings, et al., 2004).

In their study Pablo et al. (1996) discuss the role of risk, especially risk perception and risk

propensity, with regard to decision making in acquisition processes. This research enhances

existing research on mergers and acquisitions, as it encounters the individual risk behavior of

34

managers as well. Hence, in a similar framework to Pablo and Sitkin (1992) this study reveals

that acquisition decisions are influenced by problem framing and the selection of decision

makers (individuals risk behavior). Moreover, the organizational fit of an acquisition target,

its past performance and resource requirements affect the risk perception and risk propensity

with which a manager chooses an organization (Pablo, et al., 1996).

Two studies relate risk behavior to the banking sector. Alda’s-Manzano et al. (2009) suggest

that consumer innovativeness is related to perceived risk in online banking usage. Hence the

authors find that innovative consumers perceive risk in online banking to be lower. These

findings have important implications for practitioners. As consumers accept online banking,

“leading” figures can influence other individuals and reduce their risk perception as well due

to effective communication (Alda´s-Manzano, et al., 2009). The study of Cockrill et al. (2009)

reveals that risk perception and trust are important factors to determine customer satisfaction.

Therefore, this study indicates that individuals behave sensitive in the financial context. For

this reason, research, taking into account behavioral aspects with respect to financial matters

in times of a crisis, appears to be of importance for managing globalised crisis situations.

2.6. Trust

Consumer trust in services is of great importance for marketers and has been researched

extensively within the marketing context. However, one may assume that trust of consumers

in financial services and the public system affects risk behavior in times of a financial crisis as

well. Trust as a component is of great importance to the research at hand. According to Tyler

and Stanley (2007), marketing literature provides five definitions for trust. All of these

definitions define trust as the key to manage risks, uncertainty and vulnerability associated

with the exchange of goods and services. Therefore, the definitions together employ the most

important components of trust: reliability, predictability, honesty, mutuality (each party is

equally committed to the exchange), courtesy and forbearance. Moreover, trust differs

depending on the composition of involved parties (e.g., organizations, individuals). In the

financial context trust is of major importance and central to organizational, interpersonal or

inter-organizational relationships. Tyler and Stanley (2007) find that in banks and financial

institutions bankers do not completely understand the importance of trust and have no explicit

strategies to cope with this issue. Relationships are merely enhanced by chance. For this

reason it is important that the financial sector becomes aware of the centrality of building trust

and its role for customer services, service quality and the relationship building and

maintenance (Tyler, et al., 2007).

35

Considering the economic crisis, it is important to see the issue of trust. Especially when

credit is involved, organizations and individuals build relationships that depend heavily on

trust to manage risks, rely on the counterparty and predict bad debt or future payments.

Individual consumers reacted emotional and irrational to the crisis. Hence, restoring trust and

repairing the relationships is very important. This reimbursement needs to happen as soon as

possible by financial advisors and governments, as well, to reassure customers. Advisors and

politicians need to audit their own practices and redefine risk strategies. In case of effective

communication and information consumers can restore trust to the system (Gounaris, et al.,

2009).

In addition Pennings et al. (2002) focused on trust, as being crucial when consumers deal with

crisis situations and governments need to communicate strategic coping mechanisms

(Pennings, et al., 2002). Cockrill et al., as stated in section 2.5.2., perceive trust to be of great

importance to understand consumers and improve customer services in the financial industry

(Cockrill, et al., 2009). Thus, in line with the research at hand, trust may influence risk

behavior to a great extend and will be included as a determining factor.

2.7. Socioeconomic factors

As the literature review shows not only risk attitude, risk perception and the interaction of

both components play a role in individual risk behavior. In my study I want to research as

well in what way demographic and socioeconomic factors drive risk behavior. For this reason

I will review the different variables and their prospective influence on risk behavior briefly.

2.7.1. Age, Nationality & Gender

Demographic factors influence risk behavior of individuals, as can be seen in the studies

reviewed above and the research by Barber and Odean (2001). As Pennings et al. (2002)

found out, people from different nationalities react heterogeneous in situations under risk,

specifically the BSE crisis. Nevertheless, the authors concluded that Americans and the Dutch

show similar perceptions of risk in contrast to Germans. Therefore, Americans and the Dutch

perceive the risk of being contaminated with the Creutzfeld-Jacob Disease lower and are less

concerned about eating beef. This is most likely due to mistrust in the information given

through governmental agencies (Pennings, et al., 2002). Moreover, an individual’s age

influences risk behavior. Halek and Eisenhauer (2001) find that the older a person is the lower

is the risk aversion of this person. However, as a person passes the age of 65 risk aversion

becomes stronger again. This influence of age on risk behavior was also established by Morin

36

and Suarez (1983). However, the authors found that portfolio selection behavior by

individuals is subject to uniformly increasing risk aversion with age. An interesting aspect of

the demographic factor age can be concluded from the BLC and the emotional timeline, as

mentioned in section 2.3, wherefore an inclusion is essential for academic advances.

In their study on gender overconfidence Barber and Odean (2001) revealed that men and

women show different behavior with respect to stock trading. The authors state that especially

in the financial context men are more prone to overconfidence than women, which indicates

that men trade more and thus perform worse. In addition this outcome is highly significant for

single men and single women. Moreover, highly overconfident individuals tempt to invest in

high risk portfolios. For this reason Barber and Odean (2001) analyze that women do hold

less risky portfolios than men and that there is a significant difference in their attitudes toward

risk.

Recent research by Fellner and Maciejovsky (2007) finds as well that women are more risk

averse than men. The authors find that women do not engage in as much market activity as

men. However, they mention that this finding has to be regarded with caution since secondary

determinants may influence this different behavior between men and women. Considering the

finding by Barber and Odean (2001), it appears to be reasonable, as overconfidence influences

market behavior as well. Nevertheless, as my research deals with risk behavior I would expect

that gender also influences risk behavior in the financial context of credit behavior, because

women appear to be more risk averse than men.

2.7.2. Education & Income

In line with the above stated literature review, I assume that education as well as income of an

individual will affect risk behavior. Several articles include these two factors in order to

analyze individual risk behavior and find that the educational level and the level of income

and wealth impact the risk aversion an individual demonstrates. Hartog et al. (2002) state that

risk aversion declines the higher an individual’s income and educational level are. This

finding is supported by a study on asset allocation and individual risk aversion, done by Riley

and Chow (1992). Considering credit behavior of individuals I believe that education and

income may influence an individual’s behavior substantially in this context.

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2.8. Conclusion

The literature review above outlined that consumer behavior is a complex topic to consider.

Especially in light of the research on behavioral finance and the different market anomalies

we can observe that nowadays consumer behavior becomes ever more important. However, it

is difficult to find the one right answer to all questions consumer behavior raises. For this

reason I want to study consumer credit behavior in times of crisis. The literature review

outlines the manifold meaning of behavioral finance as a second pillar besides traditional

finance theories. It defines prospect theory, the BLC hypothesis, emotions, framing, heuristics

and the meaning of mental accounting. For the research at hand I believe all of these factors

loom large on the behavior in crisis situations and help to explain anomalies that occur in

today’s global markets.

Furthermore, the behavioral finance stream, especially the prospect theory, describes

individuals’ risk behavior accurately. Additionally, in the context of credit, consumers are

subject to mental accounting as well as heuristic biases, such as self control and framing, as

was discussed in this chapter. Hence, consumers will behave irrational in crisis situations.

Being confronted with an unstable financial system and different measures employed by the

governments, consumers face uncertainty. This implies that they have to make choices under

risk concerning their financials. In order for academics and practitioners to better predict and

understand consumer behavior in times of crisis, it is important to analyze consumer risk

behavior and define the factors affecting this behavior most. For this reason the research at

hand will primarily determine in how far risk attitude, risk perception, the interaction of both

factors and trust influence risk behavior of individuals in the context of credit cards. Several

pieces of research have shown that risk attitude and risk perception as well as trust are

important factors in determining consumer behavior under risk. Although, definitions may

vary, a deeper understanding of what drives consumer behavior is crucial for predictive

purposes and the development of strategies and policies in times of crisis. Further, I analyze

whether demographic factors and socioeconomic factors drive risk attitude or risk perception

and to what extent these factors influence risk behavior. In line with prior research age,

nationality, gender, education, income and trust will be considered in the analysis, to study

consumer reactions to the financial crisis.

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3. Conceptual Model

3.1. Introduction

The following section will present the conceptual model in order to conduct this research. I

will relate the risk behavior of individuals to credit card usage by following closely the

framework to Pennings et al. (2002) and Pennings and Garcia (2004). Therefore, risk behavior

is decoupled into the separate components of RA, RP and IRAP. This analysis may reveal

what drives the decision of individuals to reduce, or not, the transaction volume by means of

credit cards. Additionally, I will investigate the influence of several other socioeconomic

factors on individual risk behavior and find out whether these factors drive risk attitude, risk

perception or the interaction of both. This chapter will first explain and develop a conceptual

model, after which I will deduce the hypotheses and finally provide a graphical representation

of the model itself.

3.2. Conceptual Model

The whole framework is based on Pratt (1964) and Arrow (1971) and is in line with the model

of Pennings et al. (2002) who decouple risk behavior into the three components RA, RP and

IRAP. In order to see the effect of RA, RP and IRAP the authors define risk management to be

reflected in a risk premium 𝜋. This risk premium is a function of risk attitude, where r is the

risk aversion, the specific base situation, where W is the base wealth, and risk perception, with

a mean of 𝜀 and a 𝜎2 variance of source of additional wealth ε. An individual managing risk is

then considered to be indifferent between the exposure to risk, being involved in the risky

situation, or the mean value minus the risk premium. Expressing this relationship in form of

the expected utility model reveals the following equation as described in the literature review

above:

𝐸𝑈 𝑊 + 𝜀 = 𝑈(𝑊 𝜀𝑓 𝜀 𝑑𝜀 − 𝜋)

Rewriting this formula and including the Pratt-Arrow coefficient of absolute risk aversion the

risk premium equals 𝜋 =1

2𝜎2𝑟(𝑊). Hence, Pratt and Arrow reveal that risk management

behavior of individuals is dependent on the three components risk attitude, risk perception and

the product between them. According to Pennings et al. (2002) decoupling risk behavior into

RA and RP offers a robust segment-level conceptualization and hence a good prediction of

individual risk behavior. Traditional concepts studying perceived risk focus on the negative

outcomes of risky situations. As I desire to analyze risk behavior under uncertainty in general,

39

the framework of Pennings et al. (2002) satisfies this necessity and does not focus solely on

negative outcomes. In order to apply this framework, I conduct a survey to collect data and

will then continue with the analysis.

As an extension to the model by Pennings et al. (2002) trust is included as an independent

variable. Trust is the key to manage risk, uncertainty and vulnerability that is associated with

the exchange of goods and services. Pennings et al. (2002) stated that trust in governmental

actions impacts risk attitude or risk perception. Therefore, I add trust as a separate factor in

order to see the true impact of trust on consumer behavior.

3.3. Hypotheses

As this research analyzes individual risk behavior in the financial context of credit card

transactions, I develop hypotheses to specify my research questions. This way it is possible to

test the assumptions in the above mentioned statistical model and draw conclusions from the

results.

RA, as discussed above, reflects an individual’s predisposition to the content of a risky

situation. Hence, an individual who shows high risk attitude will be highly risk averse. For the

research at hand this indicates that a highly risk averse consumer will refuse to take on high

credit. The financial crisis induces consumers to be ever more suspicious of the financial

institutions and take care of private wealth sensibly. As the crisis showed, anyone can face

financial distress and unemployment due to sudden insolvencies and a recession. For this

reason, credit card transaction volume should be lower if a consumer refuses to take risks. In

previous literature Pennings et al. (2002), Weber et al. (1997) and Weber and Hsee (1998)

find evidence that RA significantly influences consumer behavior. According to Pennings et al

(2002). German behavior is driven generally by RA as well as RP and American behavior is

driven only by risk attitude in a food related context. Moreover, Weber and Hsee found that

cultural differences play a role when considering perceived risk attitude and perception of

risk. Perceived risk attitude does influence behavior, but is generally the same across culture,

whereas the perception of risk may differ due to different market structures etc. In another

article by Weber and Milliman (1997) the authors identified a significant relation between risk

attitude and specific domains, situational characteristics (financial, social etc.). For this reason

I expect that risk attitude influences credit card transaction volume, leading to the following

hypothesis:

40

H1: An individual who is highly risk averse will significantly reduce the credit card

transaction volume in times of crisis.

RP describes an individual’s assessment of the chance to be exposed to a specific risk. As risk

perception becomes higher, consumers consider the chance to be influenced by a specific risk

to be higher and may become less risk seeking. Any worker for example may feel exposed to

the risk of unemployment and bad debt or a self-employed may be confronted with insolvency

and financial distress. In this case an individual may perceive the risk to be exposed to risks

associated with taking on credit to be higher. With regard to previous literature, Pennings et

al. (2002) identified a significant relation between the reduction of beef consumption by

German consumers as well as Dutch consumers. Furthermore, several studies identified that

risk perception is an important factor when consumers are confronted with services (Mitchell,

et al., 1993). In case of financial services such as online banking, the influence of RP is

especially high and determines consumer satisfaction to a great extent as well (Alda´s-

Manzano, et al., 2009). Therefore, I arrive at the following hypothesis:

H2: An individual subject to high risk perception will significantly reduce the credit card

transaction volume in times of crisis.

Expecting that both risk attitude and risk perception influence consumer risk behavior one can

also expect that the interaction of both factors affects risk behavior. Studies by Pennings et al.

show, that the interaction of RA and RP indeed has a significant impact on consumer

behavior. IRAP is a major factor in the assessment of hedging behavior by management in

SMEs. Moreover, in the study “A note on modeling consumer reactions to a crisis: The case

of the mad cow disease” by Pennings et al. (2002) the affect of IRAP is significant for

American consumers. Although it does not prove to be significant for German and Dutch

consumers the interaction factor may determine consumer behavior, leading to the following

hypothesis:

H3: An individual who is highly risk averse and perceives more risk will significantly reduce

the credit card transaction volume in times of crisis.

A fourth factor identified as important for consumer behavior is trust. Several pieces of

research stated that trust is an important factor determining risk behavior. Pennings et al.

(2002) found that the trust consumers have in governmental actions in times of crisis

influences their behavior significantly. Furthermore, trust is a central factor to build

relationships in the financial sector and understand the customers and their behavior (Tyler, et

41

al., 2007). A similar conclusion is drawn by Cockrill et al. (2009), stating that trust is

important in establishing customer relationships. With regard to the banking sector Alda’s-

Manzano et al. (2009) identify that trust significantly affects customer satisfaction and

consumer behavior. For these reasons I identified the following hypothesis:

H4: An individual who has less trust in the government and financial institutions will

significantly reduce credit card transaction volume in times of crisis.

Besides the four components mentioned above, I expect that demographic and socioeconomic

factors affect risk behavior of consumers. For this reason I will analyze the influence of

several socioeconomic variables upon both RA and RP. Therefore, the following hypotheses

were developed. In order to save space each hypothesis includes a statement on risk attitude

as well as risk perception, which will be handled individually in the analysis.

Age has an impact on consumer behavior. The older a consumer is, the higher will be his risk

aversion (Morin, et al., 1983). In addition, according to the BLC, individuals may be faced

with either regret or pride at the end of the emotional time line. Hence, the older a person gets,

the less time she has to invest her income. Moreover, consumption is not based upon current

income but depends on a constant percentage of the present value of the individuals’ life

income. This indicates that age plays a significant role, as income becomes less in later stages

of a person’s life. Thus, I arrive at the following hypothesis:

H5: As an individual becomes older she will be more risk averse and perceive higher risk.

According to previous literature, gender affects risk behavior of individuals. The study “Boys

will be Boys: Gender overconfidence and common stock investment” by Barber and Odean

(2001) clearly states that women are more risk averse than men and invest in less risky

portfolios. The difference in risk attitudes towards risk is significantly different among

women and men. This result is supported by Fellner and Maciejovsky (2007). In their study

on market behavior in asset markets, women show higher risk aversion than men. However,

the authors mention a limitation that secondary determinants may influence this relationship

as well. Hence, the research at hand may show whether gender differences are in fact the

driver of risk behavior, indicating the following hypothesis:

H6: A female individual will be significantly more risk averse and will perceive significantly

higher risk than a male individual.

As was seen in the literature review, several studies reveal a substantial relationship between

nationality and risk behavior. Due to cultural and political differences individuals across

42

different nations behave differently, especially in times of crisis. According to

Pennings et al. (2002) this heterogeneous behavior was observed in their study on a food

crisis. Dutch and American consumers exhibited different risk behavior than German

consumers. The authors ascribed this finding to the trust consumers have in information from

governments and governmental actions in times of crisis. In addition, Weber and Hsee (1998)

found out that risk attitude is similar across nations whereas the perception of risk differs

across nations. They ascribe this result to the market structures of the respective country.

Chinese market structures are very different from American or German structure, which may

induce diverse risk behavior among individuals. Moreover, Weber and Milliman (1997)

specifically determined a relation between cultural difference and risk attitude an individual

holds. Therefore, I arrive at the following hypothesis:

H7: A German individual will be significantly more risk averse and will perceive significantly

higher risk.

The level of education and an individual’s income influence risk behavior. This was found in

two studies by Hartog et al. (2002) and Riley and Chow (1992). The authors determined that

risk aversion is significantly influenced by the level of income and education an individual

holds. Therefore, consumers with a high level of education are able to better assess riskiness.

Moreover, the wealthier an individual is, the lower is her risk aversion and perception due to

the better opportunity of compensating a loss more easily. In line with this previous research

the following hypotheses are phrased:

H8: An individual with higher education will be significantly more risk averse and will

perceive significantly higher risk.

H9: An individual with higher income will be significantly less risk averse and will perceive

significantly less risk

As was discussed above and in the literature review, trust is an important factor when

analyzing individual risk behavior. Therefore, trust is considered in the two OLS regressions

as well in order to determine the impact of this variable. Thus, the hypothesis reads as

follows:

H10: An individual who has less trust in the government and financial institutions will be

significantly more risk averse and perceive significantly higher risk.

43

3.4. Graphical Demonstration of the conceptual Model

The above formulated relationships and hypotheses can be graphically displayed as follows:

On the right consumer risk behavior is displayed as the decision to reduce credit card

transaction volume during the crisis. This variable depends on the four factors RA, RP, IRAP

and trust and describes the first four hypotheses of this research. The second part of this

framework, is displayed on the left side of the graphical demonstration. The seven

demographic and socioeconomic variables are depicted variables affecting RA and RP,

whereas trust represents a variable included in both analyses.

Figure 1 Individual’s decision to reduce credit card transaction volume in times of crisis

44

4. Research Design

4.1. Introduction

This chapter deals with the research design specifications. After reviewing the existing

literature and developing the conceptual model for my core variables and other influencing

variables the research design clarifies the procedures for operationalization. Therefore, I first

define the decision context of my study and proceed with the design and method on how to

collect data on risk attitude, risk perception, demographics and socioeconomic variables.

4.2. Decision Context

In order to analyze consumer credit behavior in times of crisis, I conduct my study in the

context of the current economic crisis. To better understand the crisis and credit behavior by

means of credit cards, I will elaborate on these aspects briefly before explaining the research

design in detail.

4.2.1. The economic crisis

In order to understand the context and development of the financial crisis better, I shortly

review the past happenings and draw connections to the risks and the credit card industry. The

global economic crisis is one of the worst crises after the great depression in the 1930s.

Accompanied by failures of key businesses, declining consumer wealth and governments

under pressure to support the economy financially, the world economy has slowed down and

found itself in a recession. Due to the immense internationalization and coherence between

globally operating corporations, it was possible that the US housing bubble collapsing in 2006

affected the whole world and led to the global crisis.

Reasons for the crisis are manifold. Deregulation of financial markets, innovation of financial

products, a growing housing bubble, easy accessible credit, overindebtedness among

consumers and incorrect pricing of the true risk are some of the reasons that lead to the global

collapse. The citizens are the ones to suffer from mistakes of politicians and greedy business

people. Trust in the governmental system was lost and it is not clear whether stronger

regulation can restore this trust.

In Germany the government tried to point some way with governmental aid for Opel as one of

the biggest employers in Germany. However, discussions are still going on and financial aid

is not in sight. This example illustrates the difficulty behind governmental action and

individual behavior. One part of the public perceives financial aid to be the best solution,

45

however, as other businesses are disregarded the public perceives the government to act in an

unfair way and a correct solution appears to be impossible in any case. Moreover,

governmental decisions and governmental aid needs to be accompanied by stronger

regulation. More regulation indicates, as well, that governments face higher costs and free

markets can be jeopardized. Especially regulations on prohibiting leverage or allowing for

limited leverage are enormous and constrain market actions (Statman, 2009).

Nevertheless, current credit card statistics indicate that individuals fear the risks of

overindebtedness more and more. Additionally, the use of debit cards becomes more common

among consumers. At the moment the most common type of payment in Germany is still cash

when considering the payment habits of consumers. However, debit cards are becoming more

widely accepted. The intrinsic meaning of credit cards may be different in Germany, as

German consumers seldom purchase goods or services on credit. They prefer credit lines

similar to debit cards. For this reason the popularity of debit cards is increasing. To some

degree this is also due to the easy handling and relatively high safety standards (Deutsche

Bundesbank, 2009). This trend from check writing and cash payment can be seen as well in

the U.S. Consumers are switching to more convenient payment methods. However, due to the

crisis, consumers felt higher risks in using credit cards and taking on loans. Hence, debit cards

became ever more popular to use. In combination with suitable credit lines for the customers,

debit cards increase their attractiveness even more (Benton, et al., 2007).

4.3. Research design

The research design can be defined in many ways. It poses a blueprint for the collection,

measurement and analysis of data. Therefore, the research design is an activity- and time

based plan assisting in answering the research question. Moreover, it guides the selection of

sources and types of information and defines the relationship between the different variables

by drawing a framework and outlining the procedures.

Research designs can be manifold and depend on several factors, like data collection, purpose,

scope of the study and research environment. Hence, depending on the nature and the

contribution of the research, the study is either of exploratory, descriptive, or causal nature. In

an exploratory study the researcher lacks the clear definition of the problems to be met during

the study. For this reason, the researcher then seeks to define the problems and develops a

concept by exploring the general topic. The descriptive study in contrast exhibits a clear

structure with exact hypotheses and research problems. This way, descriptive studies estimate

46

the proportions of a population and reveal relationships between the research variables.

Finally, causal studies deal with the concept of cause. They disclose the cause- and effect-

relationship in between two variables or more. This type of design is closely related to the

descriptive study design. Yet, it specifies the relationship in a better and more precise way

(Blumberg, et al., 2005).

For the study at hand the research design appears to be a descriptive design. The study is build

upon precise hypotheses and develops a relationship between different variables. More

precisely I want to develop a relationship between consumer credit behavior and risk

perception, risk attitude, the interaction of both as well as demographic and socioeconomic

variables.

4.4. Data Collection

In order to implement the research design I need to collect data. There are two types of data

and I define each in the following. Secondary data is data provided to a researcher through

former studies done by other researchers. In this study I exploited secondary literature in order

to review the existing research in the respective field and to build the conceptual model as

well as developing the hypotheses.

Primary data is the original type of data a researcher has collected in order to study a specific

field of interest. In the study at hand the primary data is collected via a personal interviews

with consumers. This data collection serves to relate the selected variables to consumer credit

behavior and provide first insight into this research field.

4.5. Sample Design

Before gathering the primary data the sample design has to be specified. Sampling is done in

order to increase the accuracy off results, increase availability of population elements, and for

a greater speed of data-collection. Hence, only some of the population elements are selected

to do the survey and not the whole population. Therefore, I specify the relevant population,

the sampling frame, the sampling technique as well as the sample size needed.

4.5.1. Relevant sample population

The relevant sample population is apparent from the research problem I posed above. In order

to research the credit behavior of consumers, all consumers will be part of my relevant sample

population (Blumberg, et al., 2005). The research will be conducted mainly among German

consumers. From theory and practice we know that German consumers behave differently and

47

are highly risk averse. Pennings et al. (2002) as well as Weber and Hsee (1998) found a

significant difference in risk behavior between various nationalities.

4.5.2. Sampling frame

The sampling frame closely depends on the population chosen and defines the list of elements

from which the sample is actually drawn. However, this list can be inaccurate in practice and

hard to define. In the study at hand, the population list consists of all consumers, who take

part in the interviews and consume commodities (Blumberg, et al., 2005).

4.5.3. Sampling technique

When sampling one can distinguish between two different representation techniques,

depending on whether members of the sample are selected by means of probability sampling

or non-probability sampling. Probability sampling takes the concept of random selection as a

basis. This technique ensures that each population element has a known non-zero chance of

being selected to be in the sample. The non-probability sampling in contrast is subjective and

arbitrary. In that case the researcher has the chance to choose the element herself randomly.

Thus probability sampling provides higher precision. Nevertheless, the study at hand applies

non-probability sampling, as I select the population elements taking part in the study on a

random basis and not on the basis of a controlled selection procedure (Blumberg, et al., 2005).

Moreover a researcher may distinguish between two forms of element selection, restricted

selection and unrestricted selection. Unrestricted selection implies that each sample element is

drawn individually from the population, whereas all other forms of sampling are restricted

sampling selection methods. As I select each sample element individually from the

population, the study at hand employs an unrestricted sample selection method (Blumberg, et

al., 2005). Therefore, I interview different consumers randomly selected from my

environment at different places and different times.

4.5.4. Sample Size

The sample size depends on various factors and should encounter some basic principles. Thus,

the researcher must consider the dispersion, or variance, of the sample when deciding on the

size. A larger sample is needed for higher precision when the variance within the population

is wide. Moreover, precision is always greater in case of a larger sample. In addition, a sample

48

has to be larger in case of a greater number of subgroups of interest within the sample

(Blumberg, et al., 2005).

Based on the conceptual model and the discussion above, the size of my sample needs to

consist of at least 150 respondents to my study, in order to still ensure sufficient precision.

However, I want to note that this sample size is restricted by time and costs issues and the

scope of this Master thesis.

4.6. Questionnaire Design

As I mentioned above, I conduct interviews in order to gather primary data. After having

specified my sample design I now specify the questionnaire design. This section defines how

the questionnaire is set up, how I collect data and presents the content of the questionnaire.

4.6.1. Questionnaire Type

There exist four different types of questionnaires: structured undisguised questionnaires,

structured disguised questionnaires, unstructured undisguised questionnaires and unstructured

disguised questionnaires. In order to collect the primary data, I use a structured undisguised

questionnaire. This type of questionnaire is standardized in that questions are listed in a

prearranged order and all elements receive the same questionnaire. Moreover, respondents are

acquainted on the purpose of the research and therefore, know the purpose of the questions

asked.

4.6.2. Data-collection approach

The method of data collection for the research at hand is the face-to-face interview method.

Therefore, I interviewed consumers and recorded the responses to collect my primary data.

This communication method offers several advantages, as it is a two-way conversation

initiated by me. Respondents can therefore, ask me in case a question is unclear. Moreover, it

is possible for me, to support respondents and as well to receive direct feedback and gather

the respondents’ behavior during the survey directly. Respondents were asked at various

places: work environment, private environment and public places.

4.6.3. Questionnaire Content

In this section I discuss the content of the questionnaire developed for the interviews. The

questionnaire was developed in English and German. Both can be seen in the Appendix 1.

The questionnaire shortly introduces the respondent to the topic and informs her about the

49

time needed and the data confidentiality. In order to avoid confusion different definitions for

payment methods are given (in line with the literature review and discovered pitfalls).

The first section then asks respondents about their credit card usage as well the risk attitudes,

risk perception and trust in governmental action and the financial system. The first question is

about whether the consumer did reduce transactions made by means of credit cards and if yes

by how much. Moreover, respondents are asked whether they switched to other payment

methods due to the financial crisis. In a second step respondents are asked to reveal

information upon their risk behavior. This part of section one is identical to the questionnaire

developed by Pennings et al. (2002) and adapted to the current context of the financial crisis.

The questions are measured on a 9-point Likert scale. This scaling is the most common type

of summated rating scale. Summated rating scales express respondents opinions based on a

favorable or unfavorable attitude towards the specific question. The scale produces interval

data and is useful to compare an individual’s opinion or score to that of a well defined sample

group. Furthermore, it expresses the respondent’s intensity of her feelings with this type of

scaling. Thus, it is the most appropriate scaling in order to define individuals’ RA and RP

(Blumberg, et al., 2005). The measure of RA is build from the following items: 1) For me

overindebtedness through paying with the credit/debit card is worth the risk (“Agree” to

“Disagree”), 2) I am …”Not willing to accept” to “Willing to accept” The risk of

overindebtedness when using credit/debit cards in times of crisis, 3) I accept the consequences

of credit/debit card overindebtedness in times of crisis (“Agree” to “Disagree”). Whereas the

measure of RP consists of the following items: 1) Due to overindebtedness, for me paying

with the credit/debit card during the crisis is …”Risky” to “Not Risky”, 2) Due to

overindebtedness, when using my credit/debit card in times of crisis, I am exposed to...”High

Risk” to “Low Risk”, 3) I think credit/debit card overindebtedness in times of crisis is risky

(“Agree” to “Disagree”).

The measure of trust is scaled on a 9-point Likert scale as well for the same reasons as above.

The questions for this measure are derived from a behavioral study by De Wulf et al. (2001)

and adapted to my study context. The items measuring trust are the following: 1) I think my

home country’s government has …”Not much Competence” to “Much Competence” to limit

financial risk issues in times of crisis, 2) Financial institutions in Germany give me a feeling

of trust regarding credit card usage in times of crisis (“Agree” to “Disagree”), 3) Financial

institutions supply trustworthy information regarding the danger of personal credit/debit card

overindebtedness in times of crisis (“Agree” to “Disagree”).

50

In the second section, the respondent is asked to provide some background information. This

background information serves to find out about the demographic variables age, gender and

nationality as well as the socioeconomic variables education, income and information. The

questions are mainly of the type simple category scale or multiple choice response scale.

These scales produce nominal data and are first of all specifically applicable to demographic

questions and questions to which the respondent is sought to select one or more alternatives

from different categories (Blumberg, et al., 2005).

4.6.4. Pre-test

Before conducting the final interviews a pre-test needs to be done in order to refine the

questionnaire and improve it. Two different ways of pre-testing exist according to Blumberg

et al. (2005). The first way is researcher pre-testing. This pre-test is done in the initial stages

to structure the questionnaire in a better way. For the study at hand the researcher pre-testing

was conducted by my supervisor. The second way to pre-test is participant pretesting. In this

case the questionnaire is tested by sample participants or participant surrogates. This method

is effective in order to identify ambiguous or unclear questions and correct for these. By

determining the approach taken for the pre-test the researcher can affect the pre-test as well.

Hence, a pre-test can be either collaborative or non-collaborative. A collaborative pre-test

implies, that participants are aware of the preliminary version. Thus, participants can critically

question the content and help to enhance the questionnaire. Under the non-collaborative

method, participants are not aware of the fact that the survey is a pre-test. This way the setting

is more realistic and interviewers can practice in a realistic setting. For the study at hand I

decided to interview 15 sample participants collaboratively. After adapting some minor

ambiguities regarding most of the times wording specifications the final questionnaire was

released to collect data.

51

5. Results and Discussion

5.1. Introduction

In the following chapter the data analysis as well as the statistical tests are introduced and

performed in order to test the hypotheses and discuss the overall problem statement of this

thesis. To perform the analyses the statistical package for social sciences (SPSS 17.0) was

used. After focusing on the descriptive statistics, I employ a factor analysis to determine the

four variables RA, RP, IRAP and Trust. Moreover, I will assess the construct reliability and

validity in order to continue with the regression models for testing the hypotheses. In the final

section of this chapter a short conclusion on the main findings is given.

5.2. Data analysis

5.2.1. Descriptive Statistics

As mentioned before data was collected by means of face-to-face interviews. For the study at

hand I was able to collect a total of 180 responses. Considering the descriptive statistics on the

collected data, the sample can be regarded as representative of the whole population. In the

following, graphs on the respective distributions are shown in order to assess the collected

data more accurately. As shown in figure 2 95 (52.8%) out of the total number of respondents

were female and 85 (47.2%) male. Considering the age, the youngest respondent was 19 years

old whereas the oldest respondent was 69 years old. The average of 36.54 years as well as

figure 3 show that the age distribution is skewed to the left, representing a young sample.

Concerning nationality the sample mainly consists of Germans (90%). The rest are various

nationalities (American, French, Dutch, etc). However, it was not possible to collect enough

non-German responses for a reliable subsample analysis, wherefore I tested this with a

dummy variable on the German population. In addition to the demographic characteristics, it

is interesting to consider educational backgrounds as well as income and the family status

(figure 4). Most respondents hold a degree of a University for Applied Sciences or a

University degree, indicating a high educational background for this sample. However, for the

statistical regression analysis those two degrees were grouped together, representing a

university degree. Interesting to see is that the income distribution is quite normal, with 30.6%

of the respondents earning € 2,000 - € 2,999. This stable distribution may be explained by the

employment statistic. About 60% indicated employee as profession. Moreover, about 10% are

students and about 10% indicated to be a public servant (Beamter) as their profession. With a

percentage of five, self-employed respondents pose a minority. Analyzing the family status, it

52

is interesting to see that 73.3% of the respondents do not have kids and 44.4% live in a

household with two persons. This is interesting as studies from governments indicate that this

is a well known development, when analyzing the population in Germany. These descriptive

statistics indicate that the sample is representative for the working population and the

statistical analysis will follow.

Figure 2 Gender Distribution

Figure 3 Age Distribution

53

Figure 4 Educational Distribution

5.2.2. Knowledge and Usage of credit cards and the crisis

Before analyzing the data, I first discuss the information and usage of credit cards in times of

crisis. As can be seen in table 1 most consumers did not indicate that they reduced their

purchases made by means of credit cards during the crisis or switched to another payment

method. This is interesting when analyzing the data with the logistic regression in a later

section. Moreover, the data reveals that most respondents were aware of the risk of

overindebtedness when using credit cards before participating in the study.

Table 1 Questions on Credit Card Usage and Knowledge

Interesting to see is the confidence in personal knowledge on the different payment methods.

Respondents indicated that they perceive their knowledge on payment methods to be quite

high. Simultaneously, respondents answered that they are not concerned to use their credit

card in times of crisis. When considering the distribution of payment method preference,

about 25% of the respondents are neutral on whether they prefer payment by means of credit

Yes (%) No(%)

5 95

4,4 95,6

81,7 18,3

Have you reduced your purchases made with

credit/debit cards during the financial crisis because of

fear for overindebtedness?

Have you switched to other payment methods during

the financial crisis to decrease new indebtedness?

Were you aware of the risk of overindebtedness?

Questions on credit card usage and knowledge

54

card or any other payment method. The distribution to the tails is almost equal with 10%

answering they prefer or do not prefer payment with credit cards.

Figure 5 Knowledge on Payment Methods

Figure 6 Concerns on Credit Card Usage

55

Figure 7 Payment Preference

In addition, respondents were asked to specify where they retrieve information on financial

matters in times of crisis. Interesting is the relatively low demand for information from

financial institutions. Respondents indicate that most information is derived from Media

(Internet, TV, etc) and newspapers. This may indicate that respondents are less trusting of

financial institutions, since the crisis affected these institutions. In order to confirm such

expectations and the hypotheses, the following section will start with the statistical analysis.

Table 2 Sources of Information for consumers

5.2.3. Factor Analysis and Data Validity

A factor analysis serves to reduce the amount of data a researcher is dealing with. By

extracting a group of highly intercorrelated items/variables, latent variables are formed.

Hence, the new construct best reflects the different items. The most common form of factor

analysis is the exploratory analysis. However, I will use a confirmatory factor analysis (CFA)

in order to evaluate each item and its contribution to each construct. Moreover, the CFA

shows how well the overall model measures the construct into the relationships between

Yes (%) No(%)

45,6 54,4

70,6 29,4

67,8 32,2

40,6 59,4

7,8 92,2

Media

Newspapers

Friends, Family and /or Relatives

Others

From which sources do you derive information on

financial Matters in times of crisis?

Financial Institutions

56

independent and dependent variables. For the factor analysis itself a principal component

analysis is most suitable. This analysis is run in SPSS and summarizes the different items into

constructs.

In order to run a factor analysis several criteria have to be considered and met: linearity,

substantial correlations and item quantity as well as sample size. Linearity is of importance as

the factor analysis seeks variables that are uncorrelated (orthogonal) in the end. Therefore, a

linear combination is identified by extracting the maximum variance from the items and

building a factor. A second factor is then extracted by combining the items of the remaining

maximum variance. This process is repeated until the predetermined amount of factors is

reached or until no more factors can be extracted from the remaining variance (Bühner, 2003).

Substantial correlation is mandatory for the analysis, as a factor analysis is only possible in

case of intercorrelation between items. These correlations indicate the strength of the

relationship among the different items and are displayed in the correlation matrix. SPSS

reveals the determinant of this matrix. The determinant should be unequal to zero, as this

would indicate the matrix to be singular (invertible). However, a determinant of one indicates

that the items have no correlation at all. Therefore, a determinant between zero and one is

suitable to do a factor analysis (Bühner, 2003).

One approach to quantify the number of correlations that can be detected in the correlation

matrix is the Kaiser-Meyer-Olkin (KMO) coefficient. The KMO coefficient measures the

sample adequacy and indicates whether the data is suitable for a factor analysis. The

following ranges give an indication of the coefficient value: below 0.50 indicates that data is

incompatible for a factor analysis; although 0.50 to 0.59 values are bad, a factor analysis is

possible; better are values above 0.60 those are considered moderate, whereas values higher

than 0.80 are considered to be good and very good for a factor analysis (Bühner, 2003).

The second test that can be used to identify substantial correlation is the Bartlett’s test of

sphericity. This test is done to check whether the correlation matrix is an identity matrix,

meaning that all correlations are equal to zero. In order to use a factor analysis one should be

able to reject the null hypothesis (correlation matrix is an identity matrix). As is the case for

many statistical tests of significance, the Bartlett’s test of sphericity benefits from a large

sample size. In case of a large sample, it is easier to reject the null hypothesis (Bühner, 2003).

The last criterion to consider for a factor analysis is the sample size and item quantity. It is

important to specify prior to the collection of data how many factors a researcher expects. In

57

order to build a reliable construct at least three items are necessary, but more are always

better. Hence, a researcher has to consider that each construct requires at least three questions,

to arrive at a reliable variable. Moreover, the item’s reliability can be checked via

communalities. Although the communality is difficult to assess for one item in one

measurement, the communality gives an indication on reliability. Communality measures the

part of variance that all items explain of one construct. Usually communality should exceed

0.60 as the higher the item’s communality is the better an item will represent the construct.

Concerning sample size, the following guidelines apply to run a factor analysis. A minimum

of 60 responses is said to be necessary in order to run a factor analysis. Whereas 100

responses are considered sufficient, 200 responses are considered to be fair and everything

above 300 responses is good or even better (Bühner, 2003).

I will now review the criteria by testing the collected data and doing a factor analysis in SPSS.

Regarding the linearity criterion, adequate linear relationships between items should in fact

exist. The questionnaire was developed in order to arrive at certain constructs for the analysis.

Therefore, I expect that linear relationships are observable for the RA, RP and Trust

constructs, which will be further confirmed in the factor analysis. When considering the

required substantial correlation, a factor analysis is suitable. The determinant given by the

correlation matrix of 0.203 is between zero and one, a desired value suitable for a factor

analysis. In order to quantify the detected correlations and the true suitability for a factor

analysis, I apply the KMO test. With an overall KMO coefficient of 0.682 the sample

adequacy is moderate. According to Bühner (2003) the value indicates that a factor analysis is

still suitable. Individual sample adequacy measurements indicate a similar picture. For all

items the coefficients are between 0.566 and 0.816, indicating some low measures, yet

suitable data for a factor analysis, as the required minimum of 0.50 is met.

In addition to the KMO coefficient the Bartlett’s test for sphericity may support the suitability

for a factor analysis. This test indicates that the null hypothesis of all correlations being equal

to zero can be rejected at a significance level of one percent. Hence, the sample is adequate

for a factor analysis when considering the required substantial correlation.

The last criteria to be met are the item reliability and the sample size. With a sample size of

180 observations, the sample size is more than sufficient and considered as good. Moreover,

for the three factors expected, each one is build of three items. Thus, the minimum of three

required items for each factor is satisfied as well. Concerning the communalities the sample

adequacy appears to be somewhat inappropriate. Although five items have values above 0.6.

58

and 0.70, four items appear to have values below 0.60. This indicates that the items do not

explain a lot of the variance of the factor extracted. Nevertheless, I will apply a factor

analysis, considering the other criteria and the purpose of this study. In addition, an

explanation will follow in the discussion later as those discrepancies may be induced by

behavioral aspects and certain behavior noticed during interviews.

In the following the actual factor analysis will be done and analyzed. There are several ways

to do a factor analysis, but as said before I will apply the most common technique, a principal

component analysis (PCA). The PCA is a mathematical factor model and is especially

suitable when the explanatory variables are closely related (e.g. near multicollinearity). In the

case at hand the PCA then extracts factors equal to the number of items from the data to build

new uncorrelated variables (RA, RP and Trust). Since this technique is purely mathematical,

factors will be extracted in descending order of importance (principal factors). Therefore, the

first extracted factors will explain most of the variance in the data set. As the other factors

will explain less and less of variance, the least important factors will be discarded in the end

(Bühner, 2003).

To determine the extracted factors, several criteria are considered. The first criterion is the

Kaiser-Guttmann Criterion or Kaiser criterion. This criterion considers the eigenvalues of one

factor. In case the eigenvalue is larger than one the factor explains more of the variance of the

data set than a standard variable. For this reason all factors with eigenvalues larger than one

are considered to be meaningful for the analysis. The eigenvalue itself indicates how much

variance the factor explains of all items and therefore declares the importance of the factor.

Another criterion to consider when analyzing the factors extracted is the graphical scree test

after Cattell. This plot shows the eigenvalues plotted against the factors graphically. By

searching for a clear cut or breaking point in the line one can determine the number of

extracted factors. After the breaking point the line will be flat and the following factors will

explain less and less of the variance, wherefore, they can be discarded. Although the scree test

is a well established method for researches it is criticized for the subjective character (Bühner,

2003).

For the data set at hand I considered both methods the Kaiser criterion and the scree test. The

Kaiser criterion extracts three factors. Thus, the test identifies three factors for which the

eigenvalues are larger than one (eigenvalues: 2,717, 1,403 and 1,055). According to the scree

test three factors are extracted until the line breaks and flattens horizontally.

59

To better interpret the factor structures the rotation methods are applied. These rotations do

not alter the item positions, but the way the items are explained by factors with the intention

to achieve the best possible explanation through the different factors. Such a depiction of

items is represented through an oblique simple structure. This means that loadings on one

factor are very high and low on other factors. Factor loadings are depicted in the factor matrix

and represent the correlations between the individual item and the factor extracted. If the

structure is not simple an interpretation is difficult. For that reason a varimax rotation is

applied. The varimax rotation produces high factor loadings on one factor and low factor

loadings on all other factors, alleviating the interpretation of factor loadings.

As can be seen in table 3 the factor loadings before rotation differ from the factor loadings

after varimax rotation. According to Hair et al. (2006) factor loadings of 0.6 and higher are

considered high, whereas loadings below 0.4 are considered as low. A minimum for factor

loadings of 0.5 is however significant. In addition, one always needs to interpret factor

loadings with regard to theory and not arbitrary because of a guideline. The factor loadings

indicate the extraction of three factors. For the construct of risk attitude the items show clear

loadings after varimax rotation with two values above 0.7 and one value close to 0.6. The

construct of risk perception is also quite clear. Two items load highly on RP whereas one item

(question on whether credit card indebtedness is considered to be risky) only loads with 0.352

on the factor. However, when conducting the interview this item was perceived in a different

way among German participants. German consumers do not commonly purchase goods on

credit, wherefore they do not perceive the risk to be exposed to overindebtedness high. This

may explain part of the contradiction and will be discussed in the last chapter (6.4.). The third

factor shows as well high loading on two items, above 0.7. Yet one item loads higher on

factor one after varimax rotation. Nevertheless, I will consider the third construct, Trust, to

consist of the last three extracted components, as indicated by the analysis without rotation.

Despite these minor limitations, I consider the loadings and outcomes from the factor analysis

satisfying. The constructs I expected and wanted to confirm are in fact determined by the

factor analysis. Before continuing with the regressions to detect relationships, data reliability

and data validity will be assessed and checked in order to provide accurate analyses.

60

Table 3 Factor Loadings before and after Varimax Rotation

5.2.4. Data Reliability

Data reliability deals with the degree of measurement accuracy and depicts the proportion of

variance of the true data from the variance of the observed data. This would mean that in case

of a reliability (𝑟𝑡𝑡) of 0.5, the test data would be explained to 50% by the systematic variance

and to 50% by the measurement error, indicating an unsatisfying reliability of data. However,

in order to circumvent the measurement inaccuracy from this test, various tests can be

applied. For the present research the internal consistency of the data needs to be estimated to

provide accurate analysis results. Internal consistency measures the relation between several

items as they represent a factor. Measurements for internal consistency are based upon item

dispersion as well as correlations and covariances and similar to the factor analysis and are an

additional data validation. One standard measurement for internal consistency is Cronbach’s

Alpha. Cronbach’s Alpha will be higher the stronger intercorrelations between several items

are. However, if item correlations are negative the alpha will be reduced (Bühner, 2003).

When analyzing inter-item correlations, correlations between 0.148 and 0.489 are detected.

Although 0.148 indicates low correlation that reduces the Cronbach Alpha, average inter-item

correlations are above 0.3 (a minimum guideline) for each construct. As can be seen in table 4

the alphas are 0.553 for the factor RP, 0.647 for the factor RA and 0.506 for the factor Trust.

These values appear to be rather poor, since values with 0.60 are considered to be acceptable

for exploratory purposes, whereas, values above 0.70 are acceptable for confirmatory

purposes and values above 0.80 are good for confirmatory purposes. Using this analysis for

Construct 1 2 3 1 2 3

Accept consequences of

indebtedness ,871 ,028 ,083 ,727 ,364 -,326

Willing to accept risk/not Willing

to accept risk ,707 ,127 ,034 ,534 ,386 -,287

Payment by credit cards is worth

the risk ,594 -,178 ,154 ,616 ,080 -,148

Credit card indebtedness is risky -,515 ,352 -,113 -,603 ,115 ,159

Exposed to high/low Risk ,058 ,815 ,002 -,273 ,770 ,025

Payment by credit card is risky -,172 ,811 -,056 -,482 ,672 ,080

Financial institutions give feeling

of trust ,491 -,292 ,370 ,465 ,013 ,656

Financial institutions provide

trustworthy Info ,076 -,130 ,790 ,465 ,013 ,656

Government has competence/no

competence ,151 ,087 ,763 ,427 ,237 ,612

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

a. Rotation converged in 4 iterations.

Component

Component MatrixRotated Component Matrixa

Trust

RA

RP

Component

61

confirmatory purposes the data appears to lack internal consistency. Even when considering

the column “Cronbach’s Alpha if the item was deleted”, which SPSS provides, values are at

0.60 only. Nevertheless, this result supports my assumption on diverse behavioral aspects in

conjunction with this study. I elaborate on this issue in the last chapter. Since the values are

yet somewhat acceptable, being close to 0.60, I will continue with the analysis despite weak

internal consistency and weak statistical data reliability.

Table 4 Data Reliability

5.3. Statistical analysis and Results

5.3.1. Logistic Regression

After assessing the data validity by means of a factor analysis as well as assessing the data

reliability with Cronbach’s Alpha, the main analysis to answer the overall research question

of this thesis: What drives consumer purchasing behavior on credit in times of crisis? will be

done. I will employ a binary logistic regression in order to see, whether risk behavior does

affect the transaction volume by means of credit cards. Furthermore, I will use ordinary least

squares (OLS) regressions to see which factors influence RA and RP.

The binary logistic regression serves to define whether individual consumers did reduce their

transaction volume by means of credit cards during the crisis. Moreover, the core objective in

this analysis is to determine the factors that affect the probability of reducing purchases made

by credit cards. Hence, the dependent variable will be binary, describing that individuals did

reduce transaction volume by means of credit cards (=0) or not (=1).

ITEMSCronbach's Alpha

if Item Deleted

Cronbach's

AlphaFACTOR

Payment by credit card is risky ,261

Exposed to high/low Risk ,483

Credit card indebtedness is risky ,607

Payment by credit cards is worth

the risk,665

Willing to accept risk/not Willing ,653

Accept consequences of

indebtedness,263

Financial institutions give feeling of

trust

,435

Financial institutions provide

trustworthy Info

,357

Government has competence/no

competence

,428

,506 Trust

,647

RP

RA

,553

62

An OLS regression would be inappropriate in this context as the dependent variable and

hence the model is not linear. Thus, it is not possible to use the general goodness of fit

measure, R squared. A linear probability model (LPM) is inappropriate as well. Although the

LPM is a model to deal with binary dependent variables in the simplest way, the model

assumes that the probability of the dependent variable is linearly related to the independent

variables.

A logistic regression in contrast transforms the regression model to a model in which the

fitted values are bounded within an interval (0,1). Therefore, it overcomes the problem to

produce negative estimated probabilities for the dependent variable or probabilities greater

than one. The fitted logistic model then appears as an s-shaped curve. The asymptotes to this

model are at 0 and 1, hence the probabilities will never actually reach either 0 or 1. The

intended regression equation is as follows (Brooks, 2008).

(1) 𝑇𝑉 0 1 = 𝛼0 + 𝛽1𝑅𝐴 + 𝛽2𝑅𝑃 + 𝛽3𝐼𝑅𝐴𝑃 + 𝛽4𝑇𝑅𝑈𝑆𝑇 + 𝜀𝑖

This equation explains the transaction volume with risk attitude, risk perception and the

interaction of both. The regression equation reveals the relationship between the binary

dependent variable 𝑇𝑉 0 1 , transaction volume, and the independent variables, RA, RP,

IRAP, and Trust. As discussed above the dependent variable 𝑇𝑉 0 1 displays whether an

individual consumer did reduce credit card transaction volume (=0) or not (=1).

By estimating the chance of a certain event to occur, the logistic regression transforms the

dependent variable into a logistic variable. This means that the natural log is taken of the

chance that the dependent variable occurs or not. Only after this transformation one can apply

the maximum likelihood estimation and run the regression. When interpreting a logistic

regression output in SPSS the outcomes appear similar to those of an OLS regression.

However, it needs to be considered that changes in the log odds of the dependent variable are

calculated in a logistic regression. In contrast, an OLS regression calculates the changes in the

dependent variable directly. Nevertheless, coefficients for the variables correspond to

coefficients in an OLS regression and can be interpreted in the same way. Additionally, the

logistic regression is subject to lighter restrictions concerning the normality assumption,

homoscedasticity and linearity.

One of the major differences between OLS regressions and logistic regressions to consider

when looking at the model fit is the R-squared. For logistic regressions an analog to the OLS

R-squared is not provided. The R-squared measures how well a sample regression fits to the

63

data (goodness of fit). Hence, it attempts to measure the fraction of the variance explained by

the independent variables. However, for a binary variable, as in this research, the variance is

most likely lower due to the dependency on the frequency distribution. The variance will be

highest in case the dichotomous variable splits in half. In case of a lopsided split the variance

will be quite low. For this reason it is not possible to compare models with differing

distributions of the dependent variable as well as logistic R-squared measures and OLS R-

squared measures. To overcome this problem, researchers attempted to find an equivalent to

measure the effect size. As such the pseudo-R-squared measures are more of a measure for

strength of association than goodness of fit. Therefore, they tempt to be misleading and

inaccurate.

In order to evaluate the model at hand, an R-squared measure is not of major importance.

However, I shortly discuss the given effect size measures. SPSS provides the -2 Log

likelihood, the Cox & Snell R-squared and the Nagelkerke R-squared as effect size measures.

In addition, the output provides the “correctly classified choices” percentage. This measure

can be regarded as the accurate and suitable measure for effect size in the case at hand and is

discussed extensively later. The -2 Log Likelihood (-2LL) is the log likelihood ratio. This

ratio specifies whether the unexplained variance in the dependent variable is significant.

Therefore, it indicates the chance of a correct prediction of the dependent variable from the

observed value of the independent variables. As a sole number the -2LL is not informative for

a goodness of it test. Generally, one can say that the -2LL statistic will decrease as the model

fit becomes better. However, the major advantage is to use the -2LL for the likelihood ratio

test to compare two models. The 30.626 -2LL for the data at hand can therefore not be

assessed on a meaningful basis. Considering the Cox & Snell R-squared of 0.203 is difficult

to interpret. The measure tempts to base the calculation on the log likelihood of the final

model against the log likelihood of the baseline model and tries to imitate the interpretation a

multiple R-squared offers. The Nagelkerke R-squared attempts to enhance the Cox & Snell R-

squared by assuring a value between zero and one. The prior measure will most likely have a

maximum below one instead of one. Thus, the Nagelkerke R-squared will yield a higher

value, which will still be lower than an OLS R-squared. Table 5 shows that Nagelkerke’s R-

squared is indeed higher at 0.619. However, due to the weak validity of pseudo R-squares

these statistics are considered with great caution and I focus more on the classification table.

64

Table 5 Logistic Model Summary Model Fit

The classification table shown below indicates the correct and incorrect predictions made with

the logistic regression. The two columns show the predicted estimates for the dependent

variable, whereas the first two rows illustrate the actual values of the dependent variable. The

last row shows the overall percentage of correctly identified cases and thus measures the

model’s effect size. If the data was subject to homoscedasticity the percentage correct would

be roughly equal for both cases. However, this is not the case and with an overall percentage

of 97.2% correctly identified cases the model appears to fit well. When comparing this rate to

the rate in the general model in step 0 of the SPSS regression of 95.0%, one can see the

improvement. Now the difference between step 0 and step 1 is the inclusion of either only the

intercept (step 0) or the predictors (step1).

Table 6 Classification Table – Goodness of Fit

Although the classification coefficients offer a better effect size measure than pseudo R-

squares, some limitations should be noted. When determining the classification coefficient,

either one (yes) or zero (no), the table does not show how close to one the predicted values

are. With a cut value of 0.50 predicted values closer to this cut value are worse than prediction

close to one. As the table does not indicate whether the values were closer to one or to the cut

value, one cannot truly assess the effect size measure. In addition, a comparison to other

models is inappropriate, as the classification table can offer very different values for different

samples. Nevertheless, with over 97% correctly classified choices the model at hand appears

to fit well and RA, RP, IRAP and Trust explain the behavior of individuals to a certain degree.

Model -2 Log likelihood

Cox & Snell R

Square

Nagelkerke R

Square

1 30,626a ,203 ,619

a. Estimation terminated at iteration number 9 because parameter estimates changed

by less than ,001.

Model Summary

Yes No

Yes 0 9 ,0

No 0 171 100,0

95,0

Yes 6 3 66,7

No 2 169 98,8

97,2

Step 0 Credit Card TV

Overall Percentage

Step 1 Credit Card TV

Overall Percentage

a. The cut value is ,500

Classification Tablea

Observed

PredictedCredit Card Transaction

Volume Percentage

Correct

65

After determining the effect size for the model, I continue with the logistic regression and test

the hypotheses. The regression output shows the following results.

Table 7 Logistic Regression Results – Regression Coefficients

Table 7 displays the independent variable coefficients (B), the standard error (S.E.), the

significance of the respective variable and the odds factor (Exp(B)). This odds factor serves

for better interpretation of results. As the model is a logistic regression, the coefficients are in

log-odds units. These units indicate by what amount the dependent variable increases or

decreases in case of a one unit increase in the independent variable, holding all other variables

constant. The odd factor is created by exponentiating the log odd coefficient. The outcome

reveals by what factor the independent variable increases or decreases the log odds of the

dependent variable.

H1: An individual who is highly risk averse will significantly reduce the credit card

transaction volume in times of crisis.

According to table 7 RA does not drive the reduction of credit card transaction volume. The

regression coefficient is insignificant. Evidently, individuals subject to high risk aversion will

not reduce credit card transaction volume during the crisis. Hence, hypothesis 1 is not

supported. This indicates that individuals do not relate credit card usage to high risks and do

not seem to think overindebtedness to be a high risk for themselves. Considering previous

research, this result contrasts the results from Pennings et al. (2002), Weber and Milliman

(1997) and Weber and Hsee (1998). In the context of food related safety issues (contaminated

meat) Pennings et al. (2002) found that generally German behavior is driven by risk attitude

as well as risk perception and that American behavior is driven by risk attitude. Weber and

Hsee find that risk attitude depends highly on the domain as well as on cultural differences

that play a role according to Weber and Milliman (1997). Therefore, considering the high

proportion of German respondents one might expect different results for other nationalities.

However, such an analysis is beyond the scope of this thesis.

B S.E. Wald df Sig. Exp(B)

RA -,502 1,170 ,184 1 ,668 ,605

RP 2,336 ,809 8,334 1 ,004 10,339

IRAP ,473 ,837 ,319 1 ,572 1,605

Trust -1,051 ,643 2,673 1 ,102 ,349

Constant 5,758 1,283 20,137 1 ,000 316,656

Variables in the Equation

Step 1

66

H2: An individual subject to high risk perception will significantly reduce the credit card

transaction volume in times of crisis.

RP significantly influences the credit card transaction volume by individuals. The odds factor

states that by the variable risk perception the odds to not reduce credit card transaction

volume during the crisis is increased by a factor of 10.339. This in turn means that individuals

will not reduce purchases made with credit cards due to their risk perception as the odds

factor is above one and the p-value is significant at the 1% level. These results are however

somewhat inconsistent with the results on risk behavior in crisis situations as analyzed by

Pennings et al. (2002). In relation to a food crisis Pennings et al. (2002) found that German

consumer behavior to reduce beef consumption is determined by risk perception as well as

risk attitude. Moreover, Dutch consumer behavior is determined only by risk perception. In

another study Mitchell and Greatorex (1993) found that services are particularly dependent

upon risk perception. This is a similar result to Alda’s-Manzano’s et al. (2009) study stating

that consumer behavior for services such as online banking are determined heavily by risk

perception, with more innovative people perceiving less risk. Lastly, risk perception and trust

were identified as factors determining consumer satisfaction to a great extent.

H3: An individual who is highly risk averse and perceives more risk will significantly reduce

the credit card transaction volume in times of crisis.

The coefficient IRAP is not significant and hypothesis 3 cannot be supported. Therefore, the

reduction of credit card transaction volume is not driven by the interaction of risk attitude and

risk perception. This contradicts prior results by Pennings and Garcia (2004). In their study

the interaction between risk attitude and risk perception depicted a major factor when

assessing hedging behavior by management in SMEs. The three segments that were identified

in the study showed that especially in segment three IRAP exhibits a fundamental factor in

determining hedging behavior. However, IRAP was found to be insignificant for Dutch as

well as German consumers in the study by Pennings et al. (2002). This outcome is in line with

the result at hand. For American consumers Pennings et al. (2002) found IRAP to be a

significant factor in determining consumer behavior, supporting the hypothesis of

heterogeneity among different nations.

H4: An individual who has less trust in the government and financial institutions will

significantly reduce credit card transaction volume in times of crisis.

The coefficient trust is not significant and hypothesis 4 cannot be supported. This means that

the reduction of credit card transaction volume is not driven by the trust consumers have in

67

financial institutions and governmental actions. This finding is inconsistent with the

deposition by Pennings et al. (2002). They found that trust is of importance in crisis

situations. Specifically, their results showed that consumer behavior is influenced by the trust

they have in the government itself and its actions to deal with the crisis. In addition, Tyler and

Stanley (2007) support the importance of trust in their study. They define trust as a central

factor to build relationships in financial contexts and understand customers and their behavior.

This is similar to the conclusion Cockrill et al. (2009) state about trust and customer

relationships. Furthermore, in “The role of consumer innovativeness and perceived risk in

online banking usage” by Alda’s-Manzano et al. (2009) trust is identified as a major factor

determining consumer satisfaction and thus consumer behavior.

5.3.2. OLS Regressions on RA and RP

In order to define how demographic and socioeconomic factors drive RA and RP, OLS

regressions will be run in a second step. These regressions will show whether secondary

factors such as age, nationality, gender, education, or income influence either RA or RP.

Again trust will be included as an independent variable to see whether trust determines RA

and RP to a significant extent. OLS regressions are the simplest form to detect a relationship

between a dependent (given) variable and one or more other variables (independent). In

economics, regression analyses are one of the most important tools to explain movements in

the dependent variable by movements in independent variables. For this reason I consider an

OLS regression to be the most appropriate analysis tool to detect hypothesized relationships.

Considering the literature review the above mentioned variables are of major interest to the

study, wherefore I will include them as independent variables in the OLS regressions.

Hence, the regression equations will be as follows (Brooks, 2008):

(1) 𝑅𝐴 = 𝛼0 + 𝛽1𝐴𝐺𝐸 + 𝛽2𝑁𝐴𝑇 + 𝛽3𝐺𝐸𝑁 + 𝛽4𝐸𝐷𝑈 + 𝛽5𝐼𝑁𝐶 + 𝛽6𝑇𝑅𝑈𝑆𝑇

(2) 𝑅𝑃 = 𝛼0 + 𝛽1𝐴𝐺𝐸 + 𝛽2𝑁𝐴𝑇 + 𝛽3𝐺𝐸𝑁 + 𝛽4𝐸𝐷𝑈 + 𝛽5𝐼𝑁𝐶 + 𝛽6𝑇𝑅𝑈𝑆𝑇

These equations explain the two factors RA and RP as dependent variables with the variables

discussed in the conceptual model “age (AGE), nationality (NAT), gender (GEN), education

(EDU), income (INC) and trust (TRUST)” as independent variables.

Before interpreting the regressions the residuals were checked for the OLS assumptions. By

testing for normality as well as examining the scatterplot for homoscedasticity and linearity, it

was ensured that none of the assumptions is violated. Therefore, a regression analysis is

68

feasible without any transformation and adjustment made to the data. Nevertheless, when

considering the model fit, table 8 indicates that the regression model for RP appears to be

inappropriate. The model for RA has an adjusted R-squared of 0.172 indicating that the

variables in the model account for 17.2% of the variations in the dependent variable, RA.

Although this percentage is quite low, it is of value to be considered moderate for a study in

the field of behavioral sciences. The negative adjusted R-squared value for the dependent

variable RP, however, indicates that the model fits the data very poorly. Due to many

marginal insignificant variables in the model a negative adjusted R-squared is possible in

regression models.

Table 8 OLS Regressions – Model Fit

Table 9 OLS Regression Results – Regression Coefficients

Table 9 shows the results of the regression analysis. As can be seen the results for RP as a

dependent variable support the previously calculated adjusted R-squared. None of the chosen

independent variables is able to explain variations in RP. However, all hypotheses will be

discussed below. First, hypothesis 5 will be analyzed.

H5: As an individual becomes older she will be more risk averse and perceive higher risk.

Model R R Square

Adjusted R

Square

Std. Error of

the Estimate Durbin-Watson

RA ,448a ,201 ,173 ,90933900 1,965

RP ,149a ,022 -,012 1,00589710 1,625

Model Summary

a. Predictors: (Constant), Trust, Gender, Nationality, Education, Age, Income

Standardized

Coefficients

B Std. Error Beta

(Constant) -2,218 ,508 -4,364 ,000

Age ,037 ,007 ,427 5,139 ,000

Gender -,029 ,146 -,014 -,196 ,845

Nationality ,460 ,230 ,138 2,002 ,047

Income ,209 ,107 ,138 1,957 ,052

Education -,014 ,065 -,017 -,210 ,834

Trust -,126 ,071 -,126 -1,760 ,080

(Constant) -,273 ,562 -,485 ,629

Age -,007 ,008 -,082 -,886 ,377

Gender ,150 ,161 ,075 ,933 ,352

Nationality ,325 ,254 ,098 1,278 ,203

Income -,062 ,118 -,041 -,523 ,602

Education ,060 ,072 ,075 ,837 ,404

Trust ,006 ,079 ,006 ,076 ,940

RP

Coefficients

Model

Unstandardized Coefficients

t Sig.

RA

69

The coefficient for age appears to be positive and significant at the 1% level for the regression

on RA. As expected the result indicates that the older a person gets the higher will be his risk

aversion. Risk attitude is driven significantly by the variable age and hypothesis 5 cannot be

rejected. This is in line with previous findings by Morin and Suarez (1983), stating that risk

aversion will increase with age. Moreover, the result may explain the BLC hypothesis.

Depending on the age and income a person’s risk attitude varies over life. Yet, in the

regression on RP the coefficient age is highly insignificant. Thus, age does not determine risk

perception and hypothesis 5 is not supported in this regression.

H6: A female individual will be significantly more risk averse and will perceive significantly

higher risk than a male individual.

Interestingly, the coefficient for gender is highly insignificant in both regression analyses on

RA and RP. Therefore, hypothesis 6 is not supported and gender drives neither risk attitude

nor risk perception. Considering some previous research done upon gender differences, the

studies by Barber and Odean (2001) as well as Fellner and Maciejovsky (2007) appear to

contrast the results. Barber and Odean state that women are less risk seeking when investing

in stocks and find a significance difference in attitudes towards risk. This behavior is

supported by the study of Fellner and Maciejovsky, stating as well that women are subject to

higher risk aversion. However, the authors mention that secondary determinants may

influence this identified relationship, wherefore, results must be treated with caution.

H7: A German individual will be significantly more risk averse and will perceive significantly

higher risk.

Regarding the regression on RA, the coefficient for nationality is positive and significant at

the 5% level. The variable is used as a dummy variable which distinguishes between German

respondents and those of other nationalities. As the coefficient is positive the outcome is

opposite to the hypothesized outcome, which underlines the different usage of credit cards by

Germans. Hence, a German individual will be significantly less risk averse. Nevertheless, the

result indicates that nationality is a major factor in determining risk attitude of an individual

and hypothesis 7 is rejected in this case. However, nationality appears to be insignificant in

the regression on RP. Hence, nationality does not drive risk perception and the hypothesis is

not supported for this regression. The result makes sense in that risk attitude depends on an

individual’s general predisposition that is influenced by environmental circumstances.

Therefore, it complies with the finding by Pennings et al. (2002). They detected that behavior

among Dutch and Americans differs strongly to the behavior of Germans. The authors

70

ascribed this difference to the trust consumers have in information from governments. Yet, the

present result shows that nationality itself may play a role in risk behavior as trust is regarded

as a separate factor.

H8: An individual with higher education will be significantly more risk averse and will

perceive significantly higher risk.

H9: An individual with higher income will be significantly less risk averse and will perceive

significantly less risk.

The coefficients for education and income are insignificant for both regressions. Therefore,

neither hypothesis 8 nor hypothesis 9 can be supported for the dependent variables RA and

RP. This finding contrasts the results of Hartog et al. (2002) and Riley and Chow (1992), who

determined that risk aversion falls as income and educational level are higher. This relation

maybe due to the wealth and lower risk for threatening situations as well as the knowledge of

probability theory and a more realistic assessment of risk. The contrasting result may partly

appear due to a sample selection bias, which will be discussed in more detail in the limitations

section in chapter six.

H10: An individual who has less trust in the government and financial institutions will be

significantly more risk averse and perceive significantly higher risk.

Trust was included as a variable in the logistic regression as well as in the OLS regression.

However, as can be seen in table 9 trust is insignificant in the in the both regressions on RA

and RP. This indicates that trust does not determine risk perception of individuals. Hypothesis

10 cannot be supported by this finding. Previous research shows opposing results as was

discussed before. Several studies find that trust is a major factor in determining consumer

behavior. Wherefore, a significant influence would have been expected for both dependent

variables.

5.4. Conclusion

The statistical section served to analyze the data within two steps. The first step gave an

indication on the true behavior during the crisis and serves to answer the main research

question. The second step serves to identify the drivers behind the two major factors.

In order to analyze the question on whether consumers reduced their credit card transaction

volume during the crisis, a binary logistic regression was run. The independent variables

included in the regression are RA, RP, IRAP and Trust, as those factors are assumed to

determine the decision on transaction volume reduction. A summary of the main findings is

71

displayed in table 10 below. Only the factor RP significantly influences consumer behavior.

RA, IRAP and Trust do not influence the dependent variable. Interesting to see is that the

coefficient for RP is positive, which means that risk perception will drive consumers to not

reduce credit card transaction volume. Nevertheless, this is not surprising when considering

the interviews and consumers’ reaction towards the questions. German respondents do not

spend the money they do not have. Hence, for most items those consumers are not willing to

purchase on credit. Evidently, the chance to be exposed to the risk of indebtedness is low and

consumers perceive their chance of personal bankruptcy as low or not existent. In addition,

the survey shows that only 2% of the respondents indicated to have reduced credit card

transaction volume, showing no change in behavior.

The logistic regression served to get a good picture of which specific risk component is

affecting consumer behavior in a crisis and whether the crisis is affecting credit card usage

behavior. However, it is as well interesting to see which factors drive the two main risk

components.

Table 10 Logistic Regression- Hypotheses Overview

In order to see the effect of demographic and socioeconomic factors on risk attitude and risk

perception OLS regressions were run. Table 11 summarizes the main findings of this analysis

and gives insight into risk behavior. Evidently, no factor was identified to significantly affect

RP, wherefore this discussion will focus on the first regression on RA.

Dependent

variable

Independent

Variable

Hypothesized

Influence

Influence Hypothesis

RA - - insignificantRP - + rejectedIRAP - + insignificantTrust - - insignificant

TV (1=No)/(0=Yes)

72

Table 11 OLS Regressions – Hypotheses Overview

The factors age and nationality significantly determine an individual’s risk attitude. The older

a consumer is the higher will be her risk aversion. Furthermore, the nationality plays a role

due to cultural differences and different behavioral patterns, Germans pursue lower risk

aversion than other nationalities. This finding is opposite to the expected relationship, but in

light of the different usage behavior of credit cards it does make sense. Therefore, nationality

does influence consumer behavior. After having a good overview of the results, the next

chapter will focus on interpretation as well as theoretical and practical contributions of this

research. In addition, I will consider some limitations and future research before giving some

concluding remarks.

Dependent

variable

Independent

Variable

Hypothesized

Sign Sign Hypothesis

Age + + not rejectedGender - - insignificantNationality - + rejectedIncome - + insignificantEducation + + insignificantTrust - - insignificantAge + - insignificantGender - + insignificantNationality - + insignificantIncome - + insignificantEducation + - insignificantTrust - + insignificant

RA

RP

73

6. Conclusion

This last chapter will serve to complete this research. The financial crisis has directed

attention to the financial sector. Regulations appear to be insufficient and consumers are

irritated and disappointed by the turmoil and financial advisors as well as governmental

actions. Although governments and financial institutions tried to apply measurements to

reduce the risk for consumers and market participants, an individual’s behavior may not

reflect this. Individual behavior in a crisis situation often differs from the true level of risk the

consumer faces. In order for advisors, businesses and governments to interpret these reactions

and behaviors in a correct manner to implement appropriate measures, consumer behavior

needs to be studied. This way, reactions in crisis situations can be predicted to reduce damage.

6.1. Main Findings

This research attempted to identify the main drivers behind consumer behavior in times of

crisis. In order to approach the main problem statement “What drives credit card usage of

consumers in times of crisis?” a model was developed. This model decoupled risk behavior

into risk attitude and risk perception. Furthermore, the interaction effect of those components

was considered as a separate factor as well as the factor trust, which consumers have in

financial institutions and governmental actions. In line with previous literature these four

factors were identified to potentially affect an individual’s decision to reduce credit card

transaction volume in a crisis. In a second step, the analysis served to define the drivers of the

two components RA and RP.

Important to note is, that on the basis of the interviews with respondents, one critical issue

was revealed. German respondents use credit cards in a very different way to other nations.

During the sample collection most respondents noted that they use a credit card in form of a

debit card. Therefore, they do not actually take on credit, as money is withdrawn directly from

their account. Respondents indicated that they would only go on credit to purchase large

things (own house, etc), but to not use their credit card for that purpose. In the U.S., credit

cards are mostly used in the real sense of credit cards, with the option to actually take on a

credit. This dissimilar usage may indicate that the German consumers represent a “saving

nation”: They will not spend money they do not own and they will only purchase “luxury

goods” in case of regular income and a safe employment position. An attitude like this is quite

exceptional in the setting of this research. Therefore, the results do reflect this particular

behavior. In addition, the low change in behavior (2% indicated a reduction in credit card

transaction volume) may indicate a risk averse attitude towards credit card usage for German

74

respondents. Hence, as indebtedness by means of credit cards is low, a change in behavior is

not induced by a crisis. This matter of fact may explain as well the weak reliability of the risk

perception construct.

As analyzed in chapter 5 only risk perception was identified to significantly affect consumer

behavior. This means that only the chance of an individual to be exposed to the risk of using

credit cards and is driving consumer behavior. The result that risk perception does not drive

consumers to reduce credit card transaction volume is however surprising and inconsistent

with previous literature. Nevertheless, considering the particular behavior of German

consumers, the result may not appear as peculiar as before. In case German consumers do not

perceive the risk of overindebtedness through credit cards as high, they will not reduce the

transaction volume. In addition, such behavior may even indicate, that the chances of being

exposed to such risk is much lower for German consumers. Therefore, German consumers

exhibit a special risk behavior with respect to the specific context and crisis.

The analysis shows that by effectively communicating information about crisis situations and

financial matters to consumers, financial institutions and governments are able to change

consumer behavior. This way consumers’ concerns are effectively abolished and the true level

of risk is communicated.

In addition, by defining the drivers of risk attitude and risk perception more precise

measurements could be implemented to avoid confusion and damage. The analysis

determined that only risk attitude is significantly determined by the age of an individual and

the nationality. The regression model for risk perception appeared to fit poorly and therefore

displayed no significant relationships. Nevertheless, the influence of age on risk perception

was positive. This result is in line with prior research and supports the hypothesis of older

people being more risk averse. Furthermore, individuals show different behavior across

different nationalities. Overall, the results from the second step indicate that measurements

implemented in crisis situations should as well consider the age and nationality of individuals

to effectively resolve the crisis.

6.2. Theoretical Contribution

After discussing the main findings, I will present the theoretical contribution of this research.

This research enhances the theory and discussion on consumer behavior in times of crisis.

Furthermore, it addresses an issue of public interest in the financial crisis, providing important

implications and directions for future research. Special attention may be directed towards the

75

controversial discussion between traditional and behavioral finance. The results of the study

indicate that irrational (behavioral) factors are important and influence consumers to a great

extent in times of crisis. Especially the decoupling of risk behavior into risk attitude and risk

perception serves a better understanding of consumer behavior in times of crisis. Risk

perception determines consumer credit behavior. This supports the research by Pennings et al.

(2002). However, the present study offers an extension to the model by including the factor

trust. This was indicated by Pennings et al. (2002) to be of importance but not tested yet.

Moreover, with the second part in the analysis, light is shed upon the factors that drive risk

behavior. The finding support previous results. Age affects risk behavior significantly as

found by Morin and Suarez (1983) already. Additionally nationality significantly determines

risk attitude. This was indicated by Pennings et al. (2002), nevertheless the focus on German

consumers offers a more precise picture on German risk behavior and encourages future

research on the cultural differences and their impact on consumer risk behavior. The inclusion

of trust as a separate factor is novel and enhances research on consumer behavior in the

financial as well as in the marketing field.

6.3. Practical Contribution

From the practical perspective the model offers an opportunity to predict consumer behavior

and valuable information for marketers, politicians and managers of financial products. The

research provides implications on whether better communication of the procedures’ and

products’ risks and advantages are needed in case risk perception weighs heavier, or if risk

has to be eliminated in case risk attitude weighs heavier. In that case only strict regulation can

be applied to control indebtedness of consumers and high transparency of financial products

needs to be employed to ensure thorough understanding. In the third case where the

interaction of both variables weighs heaviest, a hybrid solution is the best alternative to satisfy

consumers and adjust to their needs. Overall, the model may help marketers to tailor credit

card offers to their clients and considers various aspects to segment consumers. In addition,

the research analyzes consumers’ reactions in a crisis and hence, serves to develop concepts

for such critical situations.

The finding that risk perception is determining consumer credit behavior indicates that public

policy makers need to communicate the true level of risk for consumer effectively and

consistently. This can either be done through media or other public sources. However, the

most important way is via financial institutions. Those need to show transparency and inform

consumers properly and in direct ways, which may be supervised by the governments in order

76

to ensure consumer safety. Only by doing that trusting relationships can be build and

consumers will rely on given information. The recent crisis caused a lot of costs because

consumers felt betrayed and got betrayed by financial institutions. The recent crisis may be

due to ineffective and fraudulent communication of the financial sector to governments and

consumers. By analyzing consumer behavior in detail such problems can be avoided.

Furthermore, the analysis yields insight that consumer irrationality can be detected to some

extent. This helps to put the correct measurements and policies in place.

The study reveals that practitioners can achieve this even better by considering and

understanding the impact of consumer age and nationality. Older citizens behave differently

than young consumers. Sources of information are different and practitioners need to

understand this. They should adjust their procedures to reach everyone in a crisis and avoid

confusion causing high costs. In this case older consumers may need more personal advice

and more presence from professionals in order to trust and understand the products they buy.

Younger consumers instead feel acquainted with the internet and are able to search for

information themselves. However, they need to be ensured that internet safety is high and as

well need to be aware of the risks and the advantages of each product. In this sense the

transparency needs to be high for both segments. Additionally, in a globally operating world,

it is important to understand the cultural difference across nations and incorporate this into the

measurements taken to fight a crisis. German consumers are very different in their behavior

compared to other nationalities, politicians and financial institutions need to incorporate this

and provide even better information and communication in turbulent times. Hence, more

information should be communicated to the consumer. That way not only the worst news

reach a consumer but also good news and a consumer may be able to get a more objective

picture of the whole crisis situation. This might as well avoid bank runs, as was seen at the

peak of the crisis.

By consciously recognizing the drivers behind consumer behavior and applying suitable

measurements high costs can be avoided and damage from a crisis can be reduced. However,

practitioners and academics need to understand this, work together and develop appropriate

strategies considering all important factors.

6.4. Limitations and Future Research

In most cases a research is subject to several limitations because of suboptimal conditions.

Cost and time constraints are among the most common limitations for Master Theses.

However, there are numerous other limitations. For the research at hand the most considerable

77

limitations, reducing the applicability and generalization of the results, will be discussed now.

Some limitations concern the data. Therefore, the data in this research is subject to sample

selection bias. All respondents are employed or self-employed, wherefore, the sample

represents only a part of the working population. However, due to limitations for credit card

holders in Germany most providers only offer cards to individuals with a constant and steady

income on the account. Therefore, the sample may provide a good selection of individuals

owning a credit card. In addition, due to the locality and a European based university the

research focuses on German and European consumers. Despite a large sample, the collected

data could be enhanced by a larger diversity of respondents and result in a more representative

sample, leading to a better generalization of the results.

Due to the focus on German consumers, a special behavior was noticed during the collection

of data. As mentioned before German consumers from the working population appear to

budget their money conservatively. Respondents commonly do not purchase items on credit.

This supports the fact that Germany is a “saving nation”. Important to note in this context is

the weak risk perception construct. The items for this construct reflected personal as well as

the general perception to be exposed to risk. Due to the low usage of credit cards, respondents

perceived that risk perception was low for the personal risk of indebtedness. However, in

general respondents noted that they perceive the risk of indebtedness to be high. This behavior

shows inconsistency within the construct itself, but reflects the attitude of respondents very

well. Therefore, I expect that the study results in a different outcome when more respondents

from various nationalities are interviewed.

Furthermore, German respondents denoted that they use credit cards on a different basis.

Either they use a non-revolving credit card or a debit card. In both cases the consumer does

not take on a real credit. This usage is yet quite uncommon in the U.S. and indicates a very

different usage behavior, making a generalization of results problematical.

Additionally, I want to mention a possible flaw in the construct of trust. The questions

consider trust in financial institutions and governments. Considering the study context I

regarded financial institutions and governments as synonyms, which may be interpreted

differently by some respondents. Therefore, the construct may be subject to inconsistencies,

which was detected in the aftermath.

For these reasons several research opportunities exist for future research. An extension of the

database and a survey across various nations offers one of the main openings for future

78

research and might yield interesting insights into cultural differences on risk behavior.

Moreover, a different model may yield interesting insights. The double hurdle model first

defined by Cragg in 1971 describes a two stage analysis of data. Cragg assumes that

consumers make two decisions when facing a product purchase. The first stage constitutes the

decision to purchase a product, whereas the second stage displays how much of a product a

consumer will purchase. Therefore, two hurdles have to be passed in order to observe a

positive expenditure (Cragg, 1971). An analysis on this basis reveals an even better

understanding of consumer behavior in a crisis and enhances research in this field.

6.5. Concluding Remarks

In summary, this research offers insights on a new topic in consumer research. The financial

context is especially interesting and supports the importance of behavioral finance, when

analyzing market movements. The problem statement “What drives credit card usage of

consumers in times of crisis?” as well as the research questions were discussed extensively.

The research yields that consumer credit behavior in times of crisis is affected by risk

perception. Moreover, the research yields important insights into secondary factors affecting

risk behavior, namely: age and nationality. Hopefully, these results stimulate further research

in this field to observe consumer behavior and offer guidance for practitioners to deal with a

future crisis.

79

Reference List

Alda´s-Manzano, Joaquı´n, et al. 2009. The role of consumer innovativeness and perceived

risk in online banking usage. International Journal of Bank Marketing. 2009, Vol. 27, 1, pp.

53-75.

American Express. 2009. American Express reports earnings from continuing Operations of

$342 million. New York : s.n., 2009.

Barber, Brad M. and Odean, Terrance. 2001. Boys will be Boys: Gender, Overconfidence,

and Common Stock Investment. The Quarterly Journal of Economics. February 2001, Vol.

116, 1, pp. 261-291.

Benton, Marques , et al. 2007.

The Boston Fed Study of Consumer Behavior and Payment Choice:

A Survey of Federal Reserve System Employees. Federal Reserve Bank of Boston. February

2007.

Blumberg, Boris, Cooper, Donald R. and Schindler, Pamela S. 2005. Business Research

Methods. Berkshire : McGraw- Hill Education, 2005.

Brooks, Chris. 2008. Introductiory Econometrics for Finance. New York, United States of

America : Cambridge University Press, 2008.

Bühner, Markus. 2003. Einführung in die Test- und Fragebogenkonstruktion. Schweiz :

Pearson Studium, 2003.

Business Wire. 2008. New Study shows declining Credit Card Use among Americans due to

Economic Downturn. 2008.

Chiang, Thomas C. and Zheng, Dazhi. 2010. An empirical analysis of herd behavior in global

stock markets . Journal of Banking & Finance. 2010, Vol. 34, 8.

Cockrill, Antje, Goode, Mark M. H. and Beetles, Andrea. 2009. The Critical Role of

Perceived Risk and Trust in Determining Customer Satisfaction with Automated Banking

Channels. Services Marketing Quarterly. 2009, Vol. 30, 2, pp. 174–193.

Cowen, Tyler. 2009. A Simple Theory of the Financial Crisis; or, Why Fischer Black Still

Matters. Financial Analysts Journal. 2009, Vol. 65, 3.

80

Cragg, John G. 1971. Some Statistical Models for Limited Dependent Variables with

Application to the Demand for Durable Goods. Econometrica. September 1971, Vol. 39, 5,

pp. 829-844.

Dash, Eric. 2008. Consumers feel the next Crisis: It's Credit Cards. s.l. : The New York

Times, 2008.

Deutsche Bundesbank. 2009. Zahlungsverhalten in Deutschland. Frankfurt am Main :

Deutsche Bundesbank, 2009.

Durkin, Thomas A. 2000. Credit Cards: Use and Consumer Attitudes, 1970-2000. Federal

Reserve Bulletin. September 2000, 9, pp. 623-634.

Elke U. Weber, Ann- Rene' e Blais, Nancy E. Betz. 2002. A Domain-specific Risk-attitude

Scale: Measuring Risk Perceptions and Risk Behaviors. Journal of Behavioral Decision

Making. August 2002, Vol. 15, 4, pp. 263–290.

Elke U. Weber, Christopher Hsee. 1998. Cross-cultural Differences in Risk Perception, but

Cross-cultural Similarities in Attitudes Towards Perceived Risk. Management Science.

September 1998, Vol. 44, 9, pp. 1205-1217.

Fellner, Gerlinde and Maciejovsky, Boris. 2007. Risk attitude and market behavior: Evidence

from experimental asset markets. Journal of Economic Psychology. 2007, Vol. 28, 3, pp. 338–

350.

Franco Modigliani, Richard Brumberg. 1954. Utility Analysis and the Consumption Function:

An Interpretation of Cross-Section Data. [book auth.] Kurihara. Post Keynsian Economics.

New Brunswick NJ : Rutgers University Press, 1954.

Gounaris, Kathleen M. and Prout, Maurice F. 2009. Repairing Relationships and Restoring

Trust: Behavioral Finance and the Economic Crisis. Journal of Financial Service

Professionals. July 2009, pp. 75-84.

Grill, Wolfgang and Perczynski, Hans. 2003. Wirtschaftslehre des Kreditwesens. Troisdorf :

BildungsverlagEINS, 2003.

Hair J. F., Black W. C., Babin B. J. 2006. Multivariate Data Analysis (6th ed.). New Jersey,

NJ, USA : Pearson Prentice Hall, 2006.

81

Halek, Martin and Eisenhauer, Joseph G. 2001. Demography of Risk Aversion. The Journal

of Risk and Insurance. 2001, Vol. 68, 1, pp. 1-24.

Hartog, Joop, Ferrer-I-Carbonelle, Ada and Jonker, Nicole. 2002. Linking Measured Risk

Aversion to Individual Characteristics. KYKLOS. 2002, Vol. 55, 1, pp. 3-24.

Heller, Gernot and Egan, Louise. 2009. Reuters. G7 signals worst of world recession may be

over. [Online] April 24, 2009. [Cited: March 21, 2010.]

http://www.reuters.com/article/idUSTRE53M52K20090425.

Hirschman, Elizabeth. 1979. Differences in Consumer Purchase Behavior by Credit Card

Payment System. Journal of Consumer Research. June 1979, Vol. 6, 1, pp. 58-66.

Jegadeesh, Narasimhan and Titman, Sheridan. 1993. Returns to Buying Winners and Selling

Losers: Implications of Stock Market Efficiency. Journal of Finance. 48, 1993, Vol. 1, 65-91.

Jung, Alexander, et al. 2010. Spiegel Online. [Online] May 03, 2010. [Cited: June 15, 2010.]

http://www.spiegel.de/spiegel/print/d-70327158.html.

Kahneman, Daniel and Tversky, Amos. 1979. Prospect Theory: An Analysis of Decision

Making under Risk. Econometrica. March 1979, Vol. 47, 2, pp. 263-291.

Kalogeras, Nikos, et al. 2009. Understanding Heterogeneous Preferences of Cooperative

Members. Agribusiness. 2009, pp. 90-111.

Kalogeras, Nikos, et al. 2009. Understanding Heterogeneous Preferences of Cooperative

Members. Agribusiness. 2009, Vol. 25, 1, pp. 90-111.

Kara, Ali, Kaynak, Erdener and Kucukemiroglu, Orsay. 1996. An Empirical Investigation of

US Credit Card Users: Card Choice and Usage Behavior. International Business Review.

1996, Vol. 5, 2, pp. 209-230.

Kristof De Wulf, Gaby Odekerken-Schröder, Dawn Iacobucci. 2001. Investments in

Consumer Relationships: A Cross-Country and Cross-Industry Exploration. The Journal of

Marketing. October, 2001, Vol. 65, 4.

Lopes, Lola. 1987. Between Hope and Fear: The Psyhology of Risk. Advances in

Experimental Social Psychology. 1987, Vol. 20, pp. 255-25.

82

Malmendier, Ulrike and Nagel, Stefan. 2009. Depression Babies: Do Macroeconomic

Experiences Affect Risk-Taking? Working Paper Series. [Online] March 2009.

http://ssrn.com/abstract=1369049.

MasterCard Worldwide. 2009. MasterCard Incorporated Reports Second-Quarter 2009

Financial Results. New York : s.n., 2009.

Mitchell, V-W. and Greatorex, M. 1993. Risk Perception and Reduction in the Purchase of

Consumer Services. Tbe Service Industries Journal. October 1993, Vol. 13, 4, pp. 179-200.

Morin, Roger A. and Suarez, A. Fernandez. 1983. Risk Aversion Revisited. The Journal of

Finance. 1983, Vol. 38, 4, pp. 1202-1216.

Myers, Stewart C. 1983. The Capital Structure Puzzle . The Journal of Finance. 39, 1983,

Vol. 3, 575-592.

Pablo, Amy L., Sitkin, Sim B. and Jemison, David B. 1996. Acquisition Decision-Making

Processes: The Central Role of Risk. Journal of Management. 1996, Vol. 22, 5, pp. 723-746.

Pennings, Joost M.E. and Garcia, Philip. 2004. Hedging behavior in small and medium-sized

enterprises: The role of unobserved heterogeneity. Journa of Bankinf nad Finance. 2004, Vol.

28, 5, pp. 951-978.

Pennings, Joost M.E. and Grossman. 2008. Responding to crises and disasters: the role of risk

attitudes and risk perceptions. Disasters. 2008, Vol. 32, 3, pp. 434-448.

Pennings, Joost M.E. and Wansink, Brian. 2004. Channel Contract Behavior: The Role of

Risk Perceptions, and Channel Members' Market Structures. Journal of Business. 2004, Vol.

77, 4, pp. 697-722.

Pennings, Joost M.E., Wansink, Brian and Meulenberg, Matthew T.G. 2002. A note on

modeling consumer reactions to a crisis: The case of the mad cow disease. International

Journal of Research in Marketing. 2002, Vol. 19, 1, pp. 91-100.

Prelec, Drazen and Loewenstein, George. 1998. The Red and the Black: Mental Accounting

of Savings and Debt. Marketing Sciense. 1998, Vol. 17, 1, pp. 4-28.

Riley, William B. and Chow, K. Victor. 1992. Asset Allocation and Individual Risk Aversion.

Financial Analysts Journal. November 1992, Vol. 48, 6.

83

Schneider, Friedrich and Kirchgässner, Gebhard. 2009. Financial and world economic crisis:

What did economists contribute? Public Choice. June 2009, pp. 319-327.

Shefrin, H. and Statman, M. 1985. The Disposition to Sell Winners Too Early and Ride

Losers Too Long: Theory and Evidence. Journal of Finance. 1985, Vol. 40, 3, pp. 777-790.

Shefrin, Hersh and Thaler, Richard. 1988. The Behavioral Life Cycle Hypothesis. Economic

Inquiry. October 1988, Vol. 26, 4, pp. 609-643.

Shefrin, Hersh. 2002. Beyond Greed and Fear. New York : Oxford University Press, 2002.

Sitkin, Sim B. and Pablo, Amy L. 1992. Reconceptualizing the Determinants of Risk

Behavior. Academy of Management Review. 1992, Vol. 17, 1, pp. 9-38.

Sjöberg, Lennart. 2000. Factors in Risk Perception. Risk Analysis. January 2000, Vol. 20, 1.

Smidts, Ale. 1997. The Relationship between Risk Attitude and Strength of Preference: A

Test of Intrinsic Risk Attitude. Management Science. March 1997, Vol. 43, 3, pp. 357-370.

Spiegel. 2009. Spiegel Online. [Online] Juli 27, 2009. [Cited: August 3, 2009.]

www.spiegel.de/wirtschaft/0,1518,druck-638558,00.html.

Statman, Meir. 2009. Regulating financial markets: Protecting us from ourselves and others.

SSRN. [Online] January 2009. [Cited: November 12, 2009.] http://ssrn.com/abstract=1334242.

Taylor, James W. 1974. The Role of Risk in Consumer Behavior. The Journal of Marketing.

1974, Vol. 38, 2, pp. 54-60.

Thaler, Richard H. 1987. Anomalies - The January Effect. The Journal of Economic

Perspectives. Summer, 1987, Vol. 1, 1.

Tigges, Claus. 2008. Jetzt droht die Kreditkartenkrise. www.fazfinance.net. [Online] October

23, 2008. [Cited: March 25, 2010.] http://www.fazfinance.net/Aktuell/Boerse-und-

Anlage/Jetzt-droht-die-Kreditkartenkrise-4549.html.

Tversky, Amos and Wakker, Peter. 1995. Risk Attitudes and Decision Weights.

Econometrica. November 1995, Vol. 63, 6, pp. 1255-1280.

Tyler, Katherine and Stanley, Edmund. 2007. The role of trust in financial services business

relationships. Journal of Services Marketing. 2007, Vol. 21, 5, pp. 334-344.

84

Visa Inc. 2009. Visa Inc. Posts Strong Fiscal Second Quarter 2009 Earnings Results and

Reaffirms 2009 Adjusted Diluted Earnings Per Share Guidance. San Francisco : s.n., 2009.

Weber, E. U. and Bottom, W. K. 1989. Axiomatic Measures of Perceived Risk Some Tests

and Extensions. Journal of Behavioral Decision Making. 1989, Vol. 2, 2, pp. 113-131.

Weber, E. U., & Hsee, C. 1998. Cross-cultural differences in risk perception, but crosscultural

similarities in attitudes towards perceived risk. Management Science. 1998, Vol. 44, 9, pp.

1205-1217.

Weber, Elke U. and Milliman, Richard A. 1997. Perceived Risk Attitudes: Relating Risk

Perception to Risky Choice. Management Science. February 1997, Vol. 43, 2, pp. 123-144.

Woolsey, Ben and Schulz, Matt. 2009. Credit card statistics, industry facts, debt statistics .

Credit card statistics. [Online] 2009. http://www.creditcards.com.

Zinman, Jonathan. 2009. Debit or Credit? Journal of Banking and Finance. 2009, Vol. 33, 2,

pp. 358-366.

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Appendix 1

English Questionnaire

The Credit crisis – A Study on consumer behavior

Dear Participant,

As of 2007 the world got hit by the worst financial crisis until now. The past years were paved

by bad news about insolvencies, unemployment and the failure of our financial system. These

events may influence everyone and confront people with overindebtedness, bad debt and

financial distress.

We would kindly like to ask you a few questions on your credit behavior, taking about ten

minutes of your time. Therein, we will focus on credit card usage only. The term credit card is

used as an umbrella term for two basic types of credit cards, which you should both consider.

Credits cards known in the traditional sense

Debit cards: In this case money is directly withdrawn from the account of the card holder

when purchasing goods or services, similar to the common checking account (German EC-

Karte or Maestro Card with PIN).

Please, note that there are no correct answers. What matters is your personal opinion, your

perceptions and attitudes concerning credit and the risk of overindebtedness.

Of course your data will be treated confidentially!

Section 1: This section includes questions about your risk attitude and risk perception with

respect to your credit behavior. Risk behavior is related to the major risk when using credit

cards: overindebtedness and a possible private bankruptcy. According to law individuals can

file for personal bankruptcy under the bankruptcy code.

1. Have you reduced your purchases made with credit/debit cards during the financial

crisis because of fear for overindebtedness?

Yes No

1.a. If YES, by what proportion (relative to your total purchases) have you reduced your

purchases made with credit/debit cards? _____%

2. Have you switched to other payment methods (cash, check, etc.) during the financial

crisis to decrease new indebtedness ?

Yes No

IF YES, to which ones? ________________________________

3. Due to overindebtedness, for me paying with the credit/debit card during the crisis is

…..

Risky 1---2---3---4---5---6---7---8---9 Not Risky

4. For me overindebtedness through paying with the credit/debit card is worth the risk.

Agree 1---2---3---4---5---6---7---8---9 Disagree

86

5. I think my home country’s government has

Not much Competence 1---2---3---4---5---6---7---8---9 Much Competence

to limit financial risk issues to limit financial risk issues

in times of crisis in times of crisis

6. I am …….

Not willing to accept 1---2---3---4---5---6---7---8---9 Willing to accept

The risk of overindebtedness when the risk of overindebtedness when

using credit/debit cards in times of crisis using credit/debit cards in times of crisis

7. Due to overindebtedness, when using my credit/debit card in times of crisis, I am

exposed to...

Low Risk 1---2---3---4---5---6---7---8---9 High Risk

8. Financial institutions in Germany give me a feeling of trust regarding credit card usage in

times of crisis

Agree 1---2---3---4---5---6---7---8---9 Disagree

9. I am concerned about using a credit/debit card in times of crisis

Agree 1---2---3---4---5---6---7---8---9 Disagree

10. I think credit/debit card overindebtedness in times of crisis is risky

Agree 1---2---3---4---5---6---7---8---9 Disagree

11. I accept the consequences of credit/debit card overindebtedness in times of crisis

Agree 1---2---3---4---5---6---7---8---9 Disagree

12. What do you think is your chance of not being able to repay your credit in times of crisis?

Very Low 1---2---3---4---5---6---7---8---9 Very High

13. Before participating in this study, were you aware of the risk of overindebtedness?

Yes No

14. How would you evaluate your knowledge on payment methods?

No Knowledge 1---2---3---4---5---6---7---8---9 Much Knowledge

15. Financial institutions supply trustworthy information regarding the danger of personal

credit/debit card overindebtedness in times of crisis.

87

Agree 1---2---3---4---5---6---7---8---9 Disagree

16. I prefer payment by credit/debit card over other payment methods in times of crisis

Agree 1---2---3---4---5---6---7---8---9 Disagree

Section 2: This section includes questions about your background

17. How many credit/debit cards do you possess?

______ private credit/debit cards ______ business credit/debit cards

18. How many times per month do you use your private credit/debit cards on average?

Consider payments in the internet, department stores as well as e.g. gas stations

______times per month

19. How high is your credit line on your credit/debit card? In case you own more than one,

the total amount.

< € 500

€ 500 - € 999

€ 1000 - € 2.000

> € 2000

20. From which source(s) do you derive information on financial matters in times of

crisis?

Financial Institutions

Media

Newspapers

Friends, Family, and /or Relatives

Others, please specify ______________________________

21. What is your gender?

Female Male

22. What is your age?

_______ Years

23. What is your nationality? ___________________________

24. How many people are living in your household?

1

2

3

88

4

more than 4

25. Do you have children living at home?

If yes, how many _____? No

26. What is the highest level of education that you have received (finished or in progress)?

Secondary General School (Haupt/-Realschule)

High School ( A-Level, Abitur)

University of Applied Sciences (Fachhochschule)

University

Other, please specify ______________________________

27. What is your current profession (Student, employee, etc) ?

_______________________________________

28. What is your monthly after tax income?

< € 1.000

€ 1.000 - € 1.999

€ 2.000 - € 2.999

€ 3.000 - € 4.000

> € 4.000

German Questionnaire

Die Kreditkrise – Eine Studie zum Konsumverhalten

Sehr geehrter Teilnehmer,

im Rahmen meiner Masterarbeit in International Business an der Universität Maastricht

mache ich eine Umfrage über das Risikoverhalten von Verbrauchern.

Im Jahr 2007 wurde die Welt von der bisher schlimmsten Finanzkrise getroffen. Die letzten

Jahre waren von schlechten Nachrichten über Entlassungen, Insolvenzen und des Versagens

unseres finanziellen Systems geprägt. Solche Geschehnisse beeinflussen jeden und

konfrontieren Verbraucher mit Forderungsausfällen, Überschuldung und finanziellen

Engpässen.

In diesem Fragebogen möchte ich Ihnen, in ca. zehn Minuten, gerne ein paar Fragen zu Ihrem

Kreditverhalten stellen. Hierbei werde ich mich nur auf die Kreditkarten-Nutzung

beschränken. Der Begriff Kreditkarte steht für zwei Arten von Zahlungskarten, die Sie beide

beachten sollten.

89

Kreditkarte im klassischen Sinn (gewährt dem Inhaber einen Kredit), mit der Möglichkeit

Kartenumsätze monatlich abzurechnen

Debitkarte oder EC-Karte: Umsätze werden direkt vom Konto mit Überziehungskredit

abgebucht.

Bitte beachten Sie, dass es keine falschen Antworten gibt. Allein Ihre persönliche Meinung

zum Thema Kredit und Verschuldung zählt. Ihre Angaben werden natürlich vertraulich

behandelt.

Abschnitt 1: Dieser Abschnitt beinhaltet Fragen zu Ihrem Risikoempfinden bezüglich

Verschuldung. Beachten Sie, dass Überschuldung zu Zahlungsausfällen und somit zu

einer Privatinsolvenz führen kann. Seit dem 01.01.1999 gibt es nach der Insolvenzordnung

die Möglichkeit für Verbraucher Privatinsolvenz zu beantragen.

1. Bezahlen Sie aus Angst vor Überschuldung weniger Einkäufe mit der Kreditkarte/ EC-

Karte in der Finanzkrise?

Ja Nein

1.a. Wenn Ja, um wie viel Prozent (relativ zu Ihren gesamten Einkäufen) haben Sie Einkäufe

auf Kreditkarte/ EC-Karte reduziert? _____%

2. Haben Sie in der Finanzkrise zu anderen Zahlungsmethoden gewechselt, um die

persönliche Neuverschuldung zu reduzieren (Barzahlung, etc.)?

Ja Nein

2.a. Wenn Ja, zu welchen? _____________________________

3. Ich finde das Zahlen mit der Kreditkarte/EC-Karte wegen Überschuldung in der

Krise...

Riskant 1---2---3---4---5---6---7---8---9 Nicht Riskant

4. Für mich ist es das Risiko der Überschuldung wert in der Krise mit Kreditkarte/EC-

Karte zu zahlen.

Stimme zu 1---2---3---4---5---6---7---8---9 Stimme nicht zu

5. Ich denke die deutsche Regierung besitzt

Keine Kompetenz 1---2---3---4---5---6---7---8---9 Hohe Kompetenz

das finanzielle Risiko für das finanzielle Risiko für

Privatpersonen in der Krise zu limitieren Privatpersonen in der Krise zu limitieren

6. Ich bin

Nicht Bereit 1---2---3---4---5---6---7---8---9 Bereit

Das Risiko von Überschuldung Das Risiko von Überschuldung

während der Krise auf mich zu nehmen während der Krise auf mich zu

nehmen

90

7. Auf Grund von Überschuldung bei der Nutzung meiner Kreditkarte/EC-Karte in der Krise bin

ich einem

Niedrigen Risiko 1---2---3---4---5---6---7---8---9 Hohen Risiko

ausgesetzt ausgesetzt

8. Die Finanzinstitute geben mir ein Gefühl des Vertrauens bezüglich der Kreditkarten-

/EC-Karten-Nutzung in Zeiten der Krise.

Stimme zu 1---2---3---4---5---6---7---8---9 Stimme nicht zu

9. Ich mache mir Gedanken beim Einkaufen mit Kreditkarte/EC-Karte

Stimme zu 1---2---3---4---5---6---7---8---9 Stimme nicht zu

10. Ich denke, dass Kreditkarten-Überschuldung/EC-Karten-Überschuldung riskant ist

Stimme zu 1---2---3---4---5---6---7---8---9 Stimme nicht zu

11. Ich nehme das Risiko der Überschuldung durch Kreditkarten-/EC-Karten-Nutzung in Kauf

Stimme zu 1---2---3---4---5---6---7---8---9 Stimme nicht zu

12. Wie hoch ist Ihrer Meinung nach die Wahrscheinlichkeit, dass Sie Ihre Schulden

während der Finanzkrise nicht zurückzahlen können?

Sehr Gering 1---2---3---4---5---6---7---8---9 Sehr Hoch

13. Waren Sie sich des Risikos der Überschuldung durch Kreditkarten-/EC-Karten-

Nutzung bewusst, bevor Sie an dieser Studie teilgenommen haben?

Ja Nein

14. Wie schätzen Sie ihr Wissen über Zahlungsmittel ein?

Kein Wissen 1---2---3---4---5---6---7---8---9 Gutes Wissen

15. In Zeiten der Krise stellen Finanzinstitute zuverlässige Informationen bezüglich des

Risikos der Überschuldung durch Kreditkarten-/EC-Karten-Nutzung bereit.

Stimme zu 1---2---3---4---5---6---7---8---9 Stimme nicht zu

16. Bevorzugen Sie Zahlung mit Kreditkarte/EC-Karte gegenüber anderen

Zahlungsmitteln (Bar etc.) während der Finanzkrise?

Stimme zu 1---2---3---4---5---6---7---8---9 Stimme nicht zu

Abschnitt 2: Dieser Abschnitt beinhaltet Fragen zu Ihren Hintergrundinformationen.

91

17. Wie viele Kreditkarten/EC-Karten besitzen Sie?

______ private Kreditkarten/EC-Karten ______ geschäftliche Kreditkarten/EC-

Karten

18. Wie oft im Monat zahlen Sie durchschnittlich mit der Kreditkarte/EC-Karte (Bitte

beachten Sie Zahlungen im Internet, Einkaufscentern sowie z.B. Tankstellen)?

______mal pro Monat

19. Wie hoch ist Ihr monatliches Kredit-Limit (Dispositionskredit) auf Ihrer

Kreditkarte/EC-Karte? Im Fall von mehreren Karten, die komplette Summe

< € 500

€ 500 - € 999

€ 1000 - € 2.000

> € 2000

20. Von welchen Quellen beziehen Sie Information bezüglich finanzieller

Angelegenheiten in der Finanzkrise?

Finanzinstitutionen

Moderne Medien

Zeitungen

Freunde, Familie und Bekannte

Sonstiges, bitte angeben ______________________________

21. Welches Geschlecht haben Sie?

Weiblich Männlich

22. Wie alt sind Sie?

_______ Jahre alt

23. Staatsangehörigkeit: _______________________________

24. Wie viele Menschen leben in Ihrem Haushalt?

1

2

3

4

mehr als 4

25. Leben bei Ihnen noch Kinder mit im Haus?

Ja, wie viele_____? Nein

26. Was ist Ihr höchster Bildungsgrad?

92

Haupt- / Realschulabschluss

Abitur

Fachhochschulabschluss

Universitätsabschluss

Sonstige, bitte angeben ______________________________

27. Welche Tätigkeit üben Sie derzeit aus (Student, Angestellter, Beamter, etc)?

____________________________________________

28. Wie hoch ist Ihr monatliches Nettoeinkommen?

< € 1.000

€ 1.000 - € 1.999

€ 2.000 - € 2.999

€ 3.000 - € 4.000

> € 4.000

Appendix 2

Figure 8 Check for normality

93

Figure 9 Check for normality

Figure 10 Check for homoscedasticity and linearity

94

Figure 11Check for normality

Figure 12Check for normality

95

Figure 13 Check for homoscedasticity and linearity