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CHAPTER 1
INTRODUCTION
1.1 Research Background
When it comes to risk, uncertainty, or undesired outcome, people are different in the method how they resolve work-related or personal decisions. Managing risk depends on the perception towards the risk. Brown (2014) defines risk perception as a mechanism of decision making, individually based on a person’s frame of reference arise over a lifetime, among many other determinants. Thus, it will result in risk behavior in decision making. Glanz et al. (2016) define risk behavior as an individual’s inclination toward taking or avoiding risk when making s decision. Risk-taking behavior can be classified into three; which are risk lover/taker, risk-neutral and risk-averse. Understanding individual behaviors towards risk are related to the aim of understanding the economic behavior of individuals (Dohmen et al., 2011). Ojiako et al. (2014) highlighted in their study of risk perception among small and medium enterprise owner-managers the importance of general individual traits including age, gender and culture in responding to different risks.
This research will focus on one important systematic difference in risk behavior among groups. Particularly, it will focus on the gender differences in risk-taking behavior based on their risk perception. Based on previous studies, empirical research typically finds that men take more risks than women (Croson and Gneezy, 2009).

To examine gender differences in risk preferences, and to distinguish between different explanations for such differences, this study will assess both risk perception and risk-taking behavior of male and female respondents from different content domains by using Domain-Specific Risk-Taking (DOSPERT) scale. DOSPERT assesses risk perception and risk-taking behavior in five domains which are ethical, health, financial, recreational and social. The respondents for this research will be fourth-year students of Faculty of Science and Technology.

1.2 Problem Statement
Over the past century, there have been several initiatives in decreasing the gender gaps in employment and education. However, new gender gaps are starting to show. Young men seem more likely to have poor skills and lower in academic than young women. Meanwhile, young women are under-presented in the fields of physical science, mathematics, and computing (Valve, 2015). By contrast, boys seem to be better prepared to enter labor force or to apply for a job than girls (OECD, 2015).
The common stereotype is women tend to be more risk-averse than men. However, how far the statement implies the differences in background risk still not being well explained. Valve (2015) explains that gender gaps in education might be caused by differences in risk behavior and competitiveness. Thus, this paper will discuss on the gender differences in risk-taking behaviors relative to their risk perceptions.
1.3 Research Objectives
The aims of this research are
To assess risk perception of 4th year students of Faculty of Science and Technology in USIM;
To assess the risk-taking behavior of 4th year students of Faculty of Science and Technology in USIM;
To analyze the relationship between risk perception and risk behavior of 4th year students of Faculty of Science and Technology in USIM across gender.

1.4 Research Question
What is the risk perception of 4th year students of Faculty of Science and Technology in USIM?
What is the risk-taking behavior of 4th year students of Faculty of Science and Technology in USIM?
What is the relationship between risk perception and risk-taking behavior among gender of 4th year students of Faculty of Science and Technology in USIM?
1.5 Scope of Study
This research will be conducted in Malaysia. A number of 100 respondents from 4th year students of Faculty of Science and Technology from Islamic Science University in Malaysia will participate in this research. The focus of this research is to study the risk preference of 4th year students of Faculty of Science and Technology in Islamic Science University of Malaysia.

1.6 Significance of Study
Many works of literature have reported the issue on gender differences in labor market outcomes. Filippin (2016) claims that most studies of risk preferences were conducted in a finances context, yet labor economists still cite the gender differences in risk aversion as an answer for why women are under-presented in high-level jobs. OECD (2012) states that systematic differences in risk-taking between men and women could explain the gender wage gap and the discrimination of women in top-level positions jobs. Understanding why women are under-presented than men in higher-level positions and why, on average, women earn fewer than men is important for informing policy interventions aimed at ending these differences.

CHAPTER 2
LITERATURE REVIEW
2.1 Introduction
There are six sections in this chapter. First, section 2.2 discussed on past research regarding the research topic. Section 2.3 discussed on risk-taking behavior while section 2.4 discussed on risk perception. Section 2.5 discussed on the relationship between risk-taking behavior and risk perception. Section 2.6 discussed on factors in risk-taking. Lastly, section 2.7 discussed the DOSPERT scale.

2.2 Past research
Charness and Gneezy (2012) in their research on financial risk evaluation claim that women appear to be financially more risk-averse. Buser et al. (2014) found girls to be slightly more risk-averse, on average, in their sample of secondary school students in the Netherlands. De Goeij and Smedts (2008) in their research on gender differences among analyst recommendations explain that female analysts are less likely to issue extreme positive stock recommendations rather than male analysts.
According to Böheim et al. (2016), based on their research on basketball players, in critical situations, male teams choose risky strategies while female teams behave more risk-averse. Abotsi et al. (2014) found that male managers are more likely to take risk management decisions than their female counterparts. Figner and Weber (2011) report that females tend to show much lower risk behavior in some domains but not in the social domain. However, Valve (2015) argues that there are no gender differences in risk behaviors measured by the general risk question.

2.3 Risk-Taking Behavior
According to Glanz et al. (2016), risk behavior is defined as an individual’s inclination toward taking or avoiding risk when making a decision. Yildiz et al. (2014) refer to risk behaviors particularly as the differences in the way to determine and decide on risky and uncertain work or personal choices. Lee and Tseng (2015) explain risk-taking behavior as an individual’s positive or negative evaluation of controlled behavior with a perceived risk.

Through the previous studies, risk-taking behavior can be classified into three; which are risk lover/taker, risk-neutral and risk-averse. The common risk behavior that has been discussed in the previous studies are risk-averse and risk lover/ risk taker. Therefore, the main focus of risk-taking behavior for this study are risk-averse and risk-taker only, whereas the risk-neutral is excluded.

The definition of risk aversion and risk loving according to Concina (2014):
Risk aversion implies that the certainty equivalent for a risky activity is lower than its expected value because a risk-averse person will try to get rid of the risk, even if they will probably give up an expected return. In the context of risk-averse behavior, the risk premium is positive where it represents the acceptable maximum cost that a person might lose to avoid risk.
Risk-loving shows the certainty equivalent of a risk lover is always higher than the expected value. Therefore, the risk premium is negative which explains the maximum cost that the person is willing to give up to remain the risk. For risk lovers, the absolute value of risk premium represents the maximum amount that they are ready to lose when choosing between a risky activity and its expected value. They prefer to maintain risky activity rather than accept its expected value: risk is a positive characteristic.

This means that a risk taker person will always choose the activity or event that can give them highest benefit although the probability is lower, while a risk-averse person will try to avoid all the risks at any cost.

2.4 Risk Perception
Brown (2014) defines risk perception as a mechanism of decision making, individually based on a person’s frame of reference arise over a lifetime, among many other determinants. Yildiz et al. (2014) refer to risk perception as a process of psychological judgment which can reflect how people evaluate risk. Wang et al. (2016) define risk perception as an individual’s orientation of the surrounding objective risks influenced by personal intuition and speculation. Veland and Aven (2013) state that risk perception is the understanding and faith that a person or group have in relation to risks. Additionally, people’s risk perception is affected by their own risk assessment. Roszkowski and Davey (2010) explain that different individuals view the same risks in a different way.
Acar and Gö? (2011) state that risk perception includes two components of risk which are likelihood and magnitude. They refer to it as the personal assessment of the likelihood of a sure risk and how the person worried about the consequences’ magnitude. The magnitude is referred to as the amount of catastrophe if the plan does not work.

Based on the numerous definition from the previous studies, it can be seen that the concept of risk perception is complicated and multidimensional where many different elements relate to each other. This includes elements from the human agency such as beliefs, faith, judgment, and values.

2.5 The relationship between risk perception and risk behavior
Chen et al. (2015) state that researchers suggest that there is evidence shows the relationship between risk perception and decision-making, where the decision-making refer to as a person’s risk behavior. According to Riaz and Hunjra (2015), there will be a positive relationship between risk behavior and the risk perception of decision makers.

Glanz et al. (2016) in their study on individuals with multiple sclerosis state that risk behaviors and risk perceptions are associated with some demographic and clinical features of the disease. Pan and Statman (2012) claim that although wealthy and less affluent individuals may have the same risk behavior, they will likely have differences in risk perception. Therefore, the failure to distinguish between the two risk assessment variables can bias the overall evaluation of risk.

2.6 Factors in risk-taking
According to Cheung et al. (2013), demographic factors such as gender, education level, and age can affect risk-taking behaviors. Researchers have suggested that men, younger people, and well-educated person are likely to have more positive risk-taking behavior.

2.6.1 Gender
In psychological and sociological literature, it has been claimed that women are less prone than men to undertake risky actions and more inclined to perceive risks as higher than men would do (Säve-Söderbergh, 2012). Cheung et al. (2013) argue that males view risky behavior or choices more tolerable than female do. As per Rundmo and Nordfjærn (2017), males and females are concerned with different types of risks. Thus, similar risks show different meaning to them.

Scholars offered a various explanation of gender differences in how they perceive risk. One is the economic salience hypothesis where it is believed that men are more involved and affected by economic matters than women and are less concerned with other issues such as the environment. Therefore, men perceive environmental risks as less significant than women, while women perceive economic risk as less important than men. These show that both male and female interpret similar risks differently and view them from a different perspective.

2.6.2 Age
Bonem et al. (2015) suggest that age differences in risk preferences may vary across domains and may result from differing motivations. Older people identify more risk in ethical and health/safety domains but less risk from the social domain. As per Abotsi et al. (2014), age is a factor of risk perception as they explain that older people have lower risk-taking behavior than younger ones. In addition, Dohmen et al. (2011) explain that for women, the willingness to take risks decreases more rapidly than for men between age 20 and 30, and then remains steady, until it continues to decrease again from the mid-fifties onwards.

Mather et al. (2012) did an experimental study on age differences in risk seeking and found that older adults prefer sure gains and have a greater preference to avoid sure losses compared to young adults. Older people view danger at a greater level than younger ones and thus young people take more risk. In addition, younger people overestimate their capabilities and chances, which makes them take greater risk. Moreover, younger people have a positive behavior and are optimistic about the outcome (Rundmo and Nordfjærn, 2017).
2.6.3 Educational level
Risk perception varies from a person to another in educational background. Since individuals have different opinions and understanding of the circumstances based on their level in the organization, this influence their perception of the surrounding risks (Wang et al., 2016). Säve-Söderbergh (2012) declares that individuals with a college education have a higher level of risk in their portfolio compared to those with no college education.

2.6.4 Cultural
According to Ali (2017), risk perception is significantly affected by the cultural bias which is fundamental to social organization. Liu et al. (2015) state that culture influences how risk is seen. Moreover, Cheung et al. (2013) agree that Eastern people and Western people react to risk differently. They also state that individuals who depend on their friends and family are more likely to take financial risk, but are less willing to take social risk. As per Deck et al. (2012), cultural differences explain why people in China are more willing to take gambling risk than people in the US.
In Cheung et al. (2013) study to examine cultural differences (mainland China and Hong Kong) on risk perception and risk-taking behavior, they found that students from mainland China are more likely to engage risk in social, recreational, health/safety and financial behavior than students from Hong Kong. The significant factor of this study is the legal system, where the law has been strictly enforced in Hong Kong which results in the increase of awareness of risky behavior. Meanwhile, people in the mainland have become more willing to engage in such risky behavior due to the poor legal system. From this study, it can be understood that the regulation in a country or area can affect a person’s risk behavior.

2.7 DOSPERT (Adult) Scale
According to Blais and Weber (2009), Domain-Specific Risk-Taking (DOSPERT) scale is a psychometric scale that assesses risk taking in five content domains: financial, health/safety, recreational, ethical, and social. The new model DOSPERT (Adult) Scale represents the psychometric review and analysis of a revised version of the DOSPERT Scale, following up on the original work of Weber et al. (2002), inspired by the psychological risk-return model of risky choice. This revised version of the instrument is presented as the most current, updated version to be used with broader adult populations, including military ones.
The questionnaire consists of 30 items in five domains of risk which are financial, health/safety, recreational, ethics, and social. There are 6 questions for each of the domains which represent 6 item of assessments. For the Risk-Perception scale, respondents will rate their perception of the risk entailed by each risky behavior on a seven-point rating scale ranging from 1 (Not at all risky) to 7 (Extremely risky). For the Risk-Taking Behavior scale, respondents will evaluate their likelihood to engage in these risk behaviors on a three-point rating scale ranging from 1 (Extremely Unlikely) to 7 (Extremely Likely).
Sample items are as follows in Table 2.1: Domain-Specific Risk-Taking (Adult)
Table 2.1 Domain-Specific Risk-Taking (Adult)
SOCIAL Approaching your boss for a raise
Disagreeing with an authority figure on the major issue
Choosing a job that you enjoy over a secure one
Speaking your mind about an unpopular issue at work
Moving to a city far from extended family
Starting a new career in your mid-thirties
RECREATIONAL Swimming far out from shore on an unguarded lake or ocean
Going down a ski run that is too dangerous
River rafting at high water in the spring
Taking a skydiving class
Bungee jumping off a tall bridge
Piloting a small plane
FINANCIAL Betting a sum of money at the horse races
Betting a sum of money at a high-stakes poker game
Betting a some of money on the sporting event
Investing 10% of annual income in a moderate mutual fund
Investing 5% of annual income in a high-risk stock
Investing 10% of annual income in a new business venture
HEALTH Drinking heavily at a social function
Engaging in unprotected sex
Driving a car without wearing a seatbelt
Riding a motorcycle without the helmet
Driving while taking medication that gives side effect
Walking alone at night in an insecure area
ETHICAL Taking some questionable deductions on your income tax return
Having an affair with a married man/woman
Passing off somebody else’s work as your own
Revealing a friend’s secret to someone else
Leaving kids alone at home while running a duty
Keeping a wallet you found that contains $200
Source: Blais and Weber, 2009
CHAPTER 3
RESEARCH METHODOLOGY
3.1 Introduction
This chapter described the research methodology used in this research. This chapter consists of four sections. Section 3.2 discussed the sample of study which are the participants of this study; Section 3.3 discussed the data collection and its procedure; Section 3.4 discussed the variables description. Last section, 3.5, discussed on the method for data analysis for this study.

3.2 Sample of study
The sample for this study is 4th year students of Faculty of Science and Technology in Islamic Science University of Malaysia.
3.3 Data Collection
Dohmen et al. (2011) illustrate that individual risk behavior can be measured in a reliable way also with a more simple and understanding survey question. Therefore, this study will use primary data resource by using a questionnaire. The data collection procedure as follows:
The questionnaires will be distributed among the students. These students will be explained how to answer all of the questions given. Then, the students are required to answer all questions in the form in about 15 minutes. The questionnaires will be collected once the respondents have done the evaluation.

3.4 Variables Description
To examine gender differences in risk preference and to distinguish between different explanations for such differences, this study will assess the risk perceptions and risk behaviors of male and female respondents from different content domains. The questionnaire will be based on domain-specific risk-taking (DOSPERT) scale by Blais and Weber (2009), which consist of 30 items in five domains of risk which are financial, health/safety, recreational, ethics, and social. These domains are identified based on a review of past researches on risk-taking behaviors.
Prior to the past research, this study suggests some modification to the sample questionnaire proposed by Blais and Weber (2009) to the most suitable one for the sample population of this study, by taking consideration the culture and religion of the population.

For this research, the questionnaire is divided into two parts which are Part A and Part B. Part A is about the demographic information of the respondent, which has eight (8) elements. While, in Part B, there are 30 questions regarding risk perception and risk-taking behavior.
For the Risk-Perception scale, respondents will rate their perception of the risk entailed by each risky behavior on a five-point rating scale ranging from 1 (Not risky at all) to 5 (Extremely risky). For the Risk-Taking Behavior scale, respondents will evaluate their likelihood to engage in these risk behaviors on a three-point rating scale ranging from 1 (Very Unlikely) to 5 (Very Likely).
There are 6 questions for each of the domains which represent 6 item of assessments. Sample items are as follows in Table 3.1. The set of sample questionnaire used for this research presented in Appendix.
Table 3.1 Domain-Specific Risk-Taking (Adjusted)
SOCIAL Living in a city far from family
Disagreeing with a lecturer on the major issue
Choosing a career you enjoy over a prestigious one
Speaking your mind about an unpopular issue in a social occasion
Quitting a job you dislike without having a new one lined up
Arguing on an issue because of a different view
RECREATIONAL Swimming far out from shore on an unguarded lake or ocean
Going down a ski run that is too dangerous
Going camping in the wilderness
Taking a skydiving class
Bungee jumping off a tall bridge
Piloting a small plane
FINANCIAL Investing 50% of savings in a new business venture
Taking a job exclusively on a commission basis
Investing in a business that has a high probability of failing
Lending a friend a sum of money equivalent to a month income
Spending money impulsively without thinking about the consequences
Borrowing money from illegal moneylenders due to an urgency
HEALTH Sunbathing without sunscreen
Eating high cholesterol foods
Driving a car without wearing a seatbelt
Riding a motorcycle without the helmet
Driving while taking medication that may make you drowsy
Walking alone at night in an insecure area
ETHICAL Copying a software from the Internet
Cheating on exam
Forging someone’s signature
Revealing a friend’s secret to someone else
Reporting a friend for some illegal activity
Keeping a wallet you found that contains RM200
3.5 Methodology
3.5.1 Descriptive Statistic
The method that will be used in measuring the risk-taking behavior among the participants is the descriptive statistic. Descriptive statistics refer to as the quantitative method describing the main features of the data. It provides simple summaries about the sample and the observations that have been made. Central tendency and variability or dispersion are some common measures to describe the data set. From that descriptive analysis, the domains are ranked according to the frequency of the domains that the participants likely to engage. The scoring instructions for the DOSPERT scale as follows:
The DOSPERT scale contains two separate response scales: Risk-Perception and Risk-Taking Behavior.
Each DOSPERT scale item is marked S, R, F, H/S, or E (not shown on the distributed questionnaire). The letters represent the subscale to which the item belongs. Where S = Social, R = Recreational, F =Financial, H/S = Health/Safety, and E = Ethical.

Sum up rating scores across all items of a given subscale to obtain the domain score. May also divide the domain score by the number of items in the given subscale.

3.5.2 Pearson Coefficient Correlation (Gujarati and Porter, 2009)
The Pearson correlation coefficient (also referred to as the PPMCC or Pearson’s r) is a measuring method to determine the linear correlation between two variables X and Y, giving a value between +1 and -1 inclusive, where +1 is total positive correlation, 0 is no correlation, -1 is total negative correlation. It is commonly used in the sciences to measure the linear dependence between two variables.

The interpretation of the Pearson coefficient correlation as follows:
The correlation coefficient range from -1 to 1. A value of 1 indicates a perfect relationship between X and Y, with all data points lying on a line for which Y increases as X increases. A value of -1 indicates that all data points lie on a line for which Y decreases as X increases. A value of zero implies that there is no linear relationship between both variables. This method used to see the relationship between likelihood and risk perception.

CHAPTER 4
EXPECTED RESULT
4.1 Expected Result
A previous study by Valve (2015) showed that there are no gender differences in risk behaviors measured by the general risk question. The distribution of the results is almost similar for both male and female. However, most of the other studies said otherwise. Charness and Gneezy (2012) in their research on financial risk evaluation claim that women appear to be financially more risk-averse. As mentioned by Buser et al. (2014), girls are found to be slightly more risk-averse, on average, in their sample of secondary school students in the Netherlands. According to Böheim et al. (2016), based on their research on basketball players, in critical situations, male teams choose risky strategies while female teams behave more risk-averse. Besides, Abotsi et al. (2014) found that male managers are more likely to take risk management decisions than their female counterparts. Hence, by looking at the above results from previous studies, it is expected that there are gender differences in risk preference where male is likely to perceive fewer risks than female which may result in higher risk-taking.

CHAPTER 5
PROPOSAL MILESTONE AND PROJECT PLANNING
Table 5.1 shows the Gantt’s chart of the research activities and project planning of preliminary study of gender differences in risk preference.

Table 5.1: Gantt’s Chart of research activities
Research activities 2018
June Jul Aug Sept Oct Nov Dec
A B C D E F G H I J K Descriptions:
A: Literature review G: Data analysis
B: Proposal writing H: Thesis writing
C: Submitting proposal to the supervisor I: Submitting thesis draft
D: Submitting proposal to coordinator J: Thesis presentation
E: Proposal presentation K: Submitting thesis report
F: Data collection
Figure 5.1 shows the planning framework of the research activities and project planning of preliminary study of gender differences in risk preference.

Figure 5.1: Planning framework of research activities
241173088265Start
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1884045321310Introduction
Problem statement
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Scope of study
Significance
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