COGNITIVE UNDERPINNINGS OF STUDENTS’ RISK
TAKING
By Aiste Karkauskaite Student number: s2009307
Submitted to the faculty of Economics and Business In conformity with the requirements for the degree of
Master of Science in Business Administration, Specialization – Business Development
Approved:
___________________________________ dr. J.D. Hans van der Bij
Thesis Adviser, First Supervisor
___________________________________ drs. H.P. Heleen van Peet
Second Supervisor
Rijksuniversiteit Groningen, The Netherlands
II ABSTRACT
In this research individuals’ risk taking propensity is investigated. Since risky decisions taken at a right moment and in a correct way, can determine company’s financial performance and position in a market, it is highly important when developing business to understand how individuals’ character traits effect risk taking. In this study it is explored how seven different individuals’ cognitive biases - base rate regression, illusion of control, law of small numbers, regression fallacy, hindsight, overconfidence and illusory correlation - and two types of individuals’ regulatory focus – prevention and promotion - influence taking or avoiding risk. Hypotheses were tested among 85 students. It was found that individuals who are biased by hindsight, overconfidence and illusion of control biases are more likely to engage in riskier decisions. Moreover, the study showed that promotion focus has a statistically significant negative effect on risk taking propensity.
INTRODUCTION
The role of risk in organizations has gained increased recognition as the consequences of risky decisions have become more visible (Sitkin and Pablo, 1992). It became especially important to realize value and importance of individual’s willingness to take or avoid risk (MacCrimmon and Wehrug, 1990), since taken at the proper time and in a controlled way, risky decisions might not just clarify companies’ performance, but also increase revenue of the company or even open new markets. All this is highly important for developing business, since without financial success and new markets or products business development cannot occur. Moreover, a better understanding of how risky alternatives are actually evaluated may lead to the development of techniques that help decision makers better achieve their goals (Libby and Fishburn, 1977).
In this study factors, influencing risk taking, will be investigated. It will be explored, which personal characteristics and attitudes have significant influence on taking or avoiding risk. All research will be based on students’ data.
First of all, it will be examined how two types of regulatory orientation - prevention and promotion focus - influence individuals’ risk taking propensity. What is more, it will be inspected what effect seven different individual’s cognitive biases - base rate regression, illusion of control, law of small numbers, regression fallacy, hindsight, overconfidence and illusory correlation – have on risk taking propensity. Biases will be grouped in two different
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groups, depending on from which heuristic they primarily emanate – representativeness or availability heuristic (Tversky and Kahneman, 1974). Data analysis is going to be made using SPSS statistical software. This research should give an insight on causes of students’ risk taking propensity: what are the main determinants behind the phenomenon that some individuals tend to make more risky decisions than the others?
According to the expectations of the results, research questions would be as follows: First, how does individual’s regulatory focus affect risk taking propensity of students. And second, how do individual’s cognitive biases affect individual’s risk taking propensity of students. Many executives today realize how biases can distort reasoning in business. However, understanding of the effects of biases has done little to improve the quality of business decisions. (Kahneman et al, 2011). The outcome of this study will show the relationship between individual’s regulatory orientations, individual’s cognitive biases and individual’s risk-taking propensity. Usage of it should help to predict and control individual’s business decisions working independently or in a team. A recent McKinsey (2010) study showed that organizations working at reducing the effect of bias in their decision-making processes, achieved return up to seven percentage points higher. The model that will be created, shows which determinants have the highest influence on individual’s risk taking propensity. Knowing determinants, which has the highest influence on risk taking propensity, and individuals’ personal characteristics (which determinants they posses) may help managers to fit their employees with the projects they are best suited for, in order to gain better results and improve company’s performance (Libby and Fishburn, 1977).
This study will be a replication of earlier studies among entrepreneurs for the biases part (Podoynitsyna et al, 2010). Previous researches have tested the entrepreneurial personality in risk-taking propensity (Delmar, 2000). They are often characterized as extreme in terms of risk-taking propensity (Kihlstrom and Laffont 1979). Moreover, it was found that entrepreneurs exhibit greater than normal reliance on a range of cognitive biases (Baron, 2004).
However, regulatory orientation part has not been tested before and it is unknown how regulatory orientation influence risk taking propensity. This research will be a contribution to the field of decision making, strategy and management. Moreover, it will make an input to the behavioral economics for understanding economic decisions of the individuals performing economic functions and cognitive science for better understanding of decision mechanisms.
IV CONCEPTUAL MODEL AND HYPOTHESES
REGULATORY ORIENTATION
In the recent years regulatory focus theory has gained significant influence in psychology and marketing, and will likely continue to have a strong presence in the future because of its ability to explain and predict a variety of psychological processes and behaviors (Haws et al, 2010) not only in the employees dealing with their work tasks, which may be a considerable contribution in everyday tasks as well as in new project development tasks, but also in customers’ choices and decision making, which are highly important to understand, in order to gain better position in the market. For example, Lee and Aaker (2004) found that promotion-focused individuals are more influenced by requests framed in terms of gains, whereas prevention-focused participants are persuaded to a greater degree by appeals framed in terms of losses. Which leads to the conclusion, that when individual engage in activities that are consistent with their regulatory orientation, they experience augmented motivation and a sense that “it just feels right” (Aaker and Lee 2006). Given this type of feeling the customer will be satisfied with his decision and repeat purchase or give a positive feedback. Let’s shortly describe regulatory focus main principles. According to Higgins (1987) there are two types of desired end-states: the first one - ideal self-guides, which are individuals’ representation of someone’s hopes, wishes, or aspirations for them and the second - ought self-guides, which are individuals’ representation of beliefs about their duties, obligations, and responsibilities (Crowe and Higgins, 1997). It is worth to mention, that for the great majority of people hopes, wishes, or aspirations can be seen as maximal goals, while duties, obligations, and responsibilities function more like minimal goals (Brendl and Higgins, 1996). First type of characteristics mentioned above is assigned to the promotion focus, which preference, according to Bruner el al (1956), is to ensure reaching goals and insure against errors of omission. That means, that when tasks become difficult, or just following failure, promotion-focused individuals should be eager to find “hits” and ensure not to miss any possible “hit”, which means that they will try any possibility available (Crowe and Higgins, 1997). Let’s consider an example of a game, based on promotion focus regulatory type. Imagine a person playing a game, where in order to win, in the given amount of time you have to answer as many questions as possible, moreover, skipped or answered incorrectly questions will not be taken into account. The same representation fits for individuals, when
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they have a dominant promotion focused decision making type, they are eager to reach as many goals as possible, without being afraid to make a mistake or make bad decisions while reaching their desired outcomes.
The second type of characteristics, mentioned in the earlier paragraph, is classified as prevention focus, whose inclination is to ensure correct rejections and prevent errors being made (Bruner el al, 1956). That means that when tasks become difficult, or just following failure, prevention-focused individuals should be aware against mistakes and be careful trying not to make any, which will make them think twice before making any decision (Crowe and Higgins, 1997). Individuals, when they have a dominant prevention focused decision making type, are also eager to reach their goals, but the difference is, that they will try to minimize bad decisions or mistakes being made as much as possible.
Both regulatory focus decision making types are not conflicting with each other. However, usually in a particular real life situation one type of regulatory focus becomes stronger than the other.
According to that, when the task becomes difficult, individuals who predominantly possess promotion focus should be willing to take more risk, while at the same time, individuals who predominantly possess prevention focus should be more suspicious making decisions, consequently their willingness to take risk should be noticeably lower.
Therefore, it is hypothesized:
Another part of this research conceptual model (see Figure 1) consists of seven biases, which are primarily deriving from two general heuristics.
HEURISTICS AND BIASES
First of all, let’s specify the description of the heuristics in general. According to Kahneman (2003), heuristics refers to experience-based techniques for problem solving, learning, and discovery. “A judgment is said to be mediated by a heuristic when the individual assesses a
Hypothesis 1: The increase in the promotion focus will be positively associated with the level of individuals’ risk taking propensity. Hypothesis 2: The increase in the prevention focus will be negatively associated with the level of individuals’ risk taking propensity.
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specified target attribute of a judgment object by substituting a related heuristic attribute of that object which comes more readily to mind” (Kahneman, 2003). In the other words, people, whose decision making abilities are influenced by heuristics, may rely too much on their previous experience, be too spontaneous and ignore present circumstances, which could be cause of vital errors. However, Tversky and Kahneman (1974) argue that sometimes, relying on heuristics may reduce complexity of the task. It can be concluded that using heuristics in decision making is quite useful, although there is a high probability that they lead to severe and systematic errors. Biases or fallacies are errors in judgment where a heuristic is applied (Podoynitsa et al, 2010).
In this study two types of heuristics will be presented - representativeness and availability (Tversky and Kahneman, 1974). Moreover biases, primarily deriving from these heuristics, will be analyzed.
Representativeness
Representativeness is a heuristic, representing probabilistic questions about object A belonging to the class or group B. Let’s say, that Tom is a 23 year old man, unluckily he has some social problems. It’s hard for him to make new friends or to approach a girl, who he likes very much. He doesn’t like to go out and he is not very good at sports. Now let’s answer the question, how big is the probability that Tom is one of the best students in his class? In the representativeness heuristic, the probability that Tom is one of the best students in his class is evaluated by the degree to which he is representative or similar to the stereotype of the best students. Following the same example, about judgment of probabilities with insufficient amount of factors, we can stereotype nations by saying, that every Chinese knows Kung fu, or every Italian is a good cook, even more extreme, that all Romanians are vampires. This obviously is not true, although stereotypes are still alive in our cultures.
Since similarity or representativeness is influenced by much more factors than it is usually known, stereotyped approaches to the judgment of probability lead to serious errors. People, influenced by the representativeness heuristic, expect things that go together, look as though they go together. This way they are more likely to attribute a case of heartburn to spicy rather than bland food, and they are more inclined to see jagged handwriting as a sign of a tense rather than a relaxed personality (Gilovich, 1991). Because of that, decisions based on the
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representativeness heuristic often leads to mistakes and faulty conclusions being made, what is highly risky, therefore it can be hypothesized:
The four biases, which are primarily originating from the representativeness heuristic, are law of small numbers, base rate regression, illusion of control and regression fallacy (Tversky and Kahneman, 1974).
Law of Small Numbers
Law of small numbers, which may also be known as sample size fallacy or belief in the law of small numbers. It concerns the phenomenon that individuals use a limited amount of informational inputs to draw conclusions, even if that was emphasized in the formulation of a problem (Hogarth, 1980; Tversky and Kahneman, 1974; Podoynitsa et al, 2010).
Sample size fallacy may be considered as relying on an insufficient amount of sources while writing reports for the university or relying on one source of information while making important decisions in life. Individuals with high levels of sample size fallacy are generalizing facts independently from the information these small samples have. As a result, they are unaware of risks their decisions contain of and are tending to take more risk compared to those who have low level of belief in law of small numbers bias (Podoynitsa et al, 2010). As a result, this first bias of the representativeness heuristic is expected to have positive influence on individuals risk taking propensity.
Base rate regression
Base rate regression, also known as base rate fallacy, occurs when irrelevant information is used to come up with the conclusion ignoring available statistical information about prior probabilities (Tversky and Kahneman, 1974; Podoynitsa et al, 2010).
Individuals with high levels of base rate fallacy would ignore existing statistical information and base their decision on unnecessary additional information. Decisions based on needless information, which might be contrary to the available statistical information, may lead to serious errors. Decision making, ignoring important relevant information is highly risky, since most probably it will lead to an error. Thus it is expected that individuals with a high level of
Hypothesis 3: Biases, primarily originating from the representativeness heuristic, will be positively associated with the level of individuals’ risk taking propensity.
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base rate fallacy, compared to individuals with a low level of base rate regression bias, will tend to take more risks.
Illusion of control
The third bias primarily deriving from the representativeness heuristic is illusion of control. This bias occurs when individuals overestimate their skills and ability to cope with or predict future events in the situations, where chance plays a large part and outcomes are uncontrollable (Langer, 1975; Simon et al, 1999).
Individuals, who belief in their abilities to control unpredictable situations, will make more optimistic decisions, than individuals with a low level of unrealistic control, which may lead to the more risky decisions (Simon et al, 1999). According to that, it is expected, that individuals, influenced by illusion of control bias will have higher risk taking propensity. Regression fallacy
There are a lot of studies controversially interpreting regression fallacy. Older researches, such as Tversky and Kahneman (1974) argue, that it might be compared with the regression to the mean, others, such as Gilovich (1991), state, that it is due to erroneous causes assigned to the outcomes. In this study it was chosen to study the more recent description of regression fallacy, though let’s at first analyze both options possible.
On the one hand, regression fallacy occurs when looking at two unrelated measurements, it may be seen that first measurement was more extreme and the second one moved closer to the average. That means, for example, that if a student performed extremely well on a test first time, it is more likely that the second time he will perform worse and vice versa. In other words, results of the second test will move closer to the mean (Tversky and Kahneman, 1974; Podoynitsa et al, 2010, Carree and Klomp, 1996). Popular saying “life is like a rollercoaster” may be a good illustration of regression fallacy, for the reason that in the normal course of life bad times are followed by good times, rainy days are followed by sunny ones, moving everything closer to the average.
On the other hand, regression fallacy occurs when looking at two events, it is concluded that one caused the other, when in fact there is no causal relation (Gilovich, 1991; Schaffner, 1985). For example, if football player Nick suffers from knee injury and one day pain gets so bad that he decides to visit a doctor, after that symptoms of the illness reduces. The fallacy is
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to assume that the pain reduced just because of the therapy effectiveness, when in reality the person may experience symptomatic relief due to the natural recovery from or natural raise and fall of the pain.
Individuals, who fail to see the regression phenomenon, may fail in making right conclusions about successful and unsuccessful projects, as well as recognizing causes of the occurred outcome of certain actions. This way, individuals, with the higher level of regression fallacy are expected to make incorrect causal links between their actions and performance. Making judgments based on inaccurately understood cause-outcome relationships is highly risky as it sharply increases probability of failure (Podoynitsa et al, 2010). Therefore, it is expected that individuals, with a higher level of regression fallacy will tend to take more risky decisions than those who are able to see regression phenomenon.
Availability
Availability is a heuristic, based on phenomena where people assess the frequency of a class or the probability of an event by the ease with which instances or occurrences can be brought to mind (Tversky and Kahneman, 1974). In general, frequent events are easier to recall or imagine than uncommon once. For example, individuals can evaluate divorce rates in a given community by recalling divorces among friends. In this case, estimation of the frequency of an event is mediated by an assessment of availability (Tversky and Kahneman, 1973). Another example could be, that asked about mortality rates from hurricane and lung cancer, individuals will rate hurricane higher, just because it is shown more often on the news, this way they will recall hurricane accidents better and will rate mortality rate higher, even though it is not true.
However, availability is influenced by much more factors which are unrelated to actual frequency. This means, that if the availability heuristic is applied, then such factors will affect the perceived frequency of events, which will automatically lead to systematic errors. Consequently, since every individual’s experience is unique and recalled frequency of events cannot be predicted, the use of the availability heuristic will lead to highly unpredictable and risky decisions.
X Therefore it is hypothesized:
The three biases, which are primarily originating from the availability heuristic, are hindsight, overconfidence and illusory correlation (Tversky and Kahneman, 1974).
Hindsight;
The first bias, primarily deriving from availability heuristics is hindsight bias. It occurs, when individuals remember their prediction about the event as more accurate after a certain outcome occurs than it actually was (Bernstein et al, 2011). This erroneous feeling is responsible for making mistakes in understanding and learning from the past. As a result, individuals, who are not capable of understanding their mistakes in previous actions, are doomed to make them in a future again (Firschhoff, 1982). According to Podoynitsa et al (2010), individuals with a high level of hindsight bias tend to take all the glory in a case of success and blame circumstances in a case of failure. Consequently, having a wrong impression about the factors which lead to success or failure will cause influencing wrong factors. Decision making based on manipulations with incorrect factors is highly risky and most probably leading to a failure. As long as individuals are not aware of which aspects have the highest influence on a success, they are tending to make more risky decisions.
Overconfidence;
Second bias, primarily deriving from availability heuristics is overconfidence. It occurs, when an individual fails to know the limits of one’s knowledge (Russo and Schoemaker, 1992). To put it into other words, when they are extremely sure that they are right when they are actually wrong (Podoynitsa et al, 2010). Moreover, they do not realize that they are being overconfident (Tversky and Kahneman, 1974). Therefore, by taking their assumptions as facts and by not realizing that they are being overconfident, individuals erroneously conclude, that certain actions are not risky (Simon et al, 1999). It suggests that individuals with a high level of overconfidence are tending to overvalue their decision taking and personal abilities in a certain situations and take more risky and less likely to succeed decisions.
Hypothesis 4: Biases, primarily originating from the availability heuristic, will be positively associated with the level of individuals’ risk taking propensity.
XI Illusory correlation;
The last bias, primarily deriving from availability heuristics is illusory correlation. It occurs when two events are incorrectly seen as co-occurrence. It may be caused by the perception of how frequently two events co-occur (Tversky and Kahneman, 1974; Podoynitsa et al, 2010). For example, if every time Eve leaves the house it starts raining, she will start incorrectly thinking that these two events are correlated. Following the example, individuals with high illusory correlation bias may fail to see accurate relationships between events, which may lead to errors choosing wrong strategies and making fruitless decisions. Since individuals, who have incorrect perceptions about the relationship between two events, are more likely to fail in judging possible consequences of their or other person’s actions, they are more likely to take risky decisions leading to a failure, thus they have higher risk taking propensity.
Availability heuristic. Includes biases: hindsight overconfidence illusory correlation Representativeness heuristic. Includes biases:
law of small numbers base rate regression illusion of control regression fallacy Promotion focus Prevention focus Risk-‐taking propensity
Figure 1: Conceptual model
XII METHODOLOGY
DATA COLLECTION
It was chosen to make a survey based on a students data, for the reason that this way participants would be easier to approach and data collection would be faster and cheaper. For this survey students were selected randomly, by asking everyone, who was willing to fill out the questionnaire. The majority of respondents were studying in Business and Economics faculty (60% of all respondents), second faculty, judging by its popularity in the survey, was Natural Science faculty (30% of all respondents). 52% of the all sample were Master students, 38% - Bachelor students and 5% - PhD students. The majority of students were 23-24 years old, however the age of respondents’ differed from 19 to 45 years old. There was almost equal number of male (51%) and female (49%) respondents, who filled out the questionnaire.
The questionnaire was reachable in two ways - online and the regular way – both of them were completely identical, written in English. The questionnaire was being made at the beginning of summer 2011. However, since the majority of the students left the city for a summer holiday, response rate at the first part of data collection, which was held at the end of June, was extremely low. The second part of the data collection started in autumn 2011. Then all necessary data was successfully collected from more than 85 respondents. An average respondent took no longer than twenty minutes to fill out the questionnaire. After that, most of them expressed interest in the thesis subject and asked for more information about the actual meaning of the questions being asked.
MEASUREMENTS
In this study existing cases and scales from the literature were being used. Because of the questionnaire length limitation, questions with the highest factor loadings in former studies were chosen. Some of the cases were adopted to fit more to the student situation. Ten variables were measured: individual’s regulatory orientations: promotion and prevention focus, individual’s cognitive biases: base rate regression, illusion of control, law of small numbers, regression fallacy, hindsight, overconfidence and illusory correlation, and individual’s risk-taking propensity (Table 1).
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Independent Variable: Type and number of questions: Prevention focus Five Likert scale questions*
Promotion focus Five Likert scale questions*
Hindsight bias Five scale questions (difficult general knowledge questions)
Overconfidence bias Five scale questions (difficult general knowledge questions)
Law of Small Numbers Five Likert scale questions
*
Four scenario questions Base Rate Regression Four scenario questions Illusion of Control Five Likert scale questions*
Illusory Correlation Five Likert scale questions*
Regression Fallacy Five Likert scale questions*
Dependent Variable:
Risk Taking Propensity
Ten Likert scale questions*
One multiple choice scenario question (ten points scale)
Four scenario questions
* - 5-point Likert scale
Table 1.: Questions for the variables
Mediating variables
Individual’s regulatory orientations
To measure an individual’s regulatory orientation - prevention and promotion focus variables - 5 point Likert scale questions were chosen from Haws et al (2010). The scale contains five questions for each of the variables. It is a composition of six other frequently used scales, such as RFQ (Higgins et al, 2001), BIS/BAS (Carver and White, 1994), Lockwood scale (Lockwood et al, 2002). Though, it overcomes the limitations from which other scales commonly suffer. The main principle in evaluating students’ regulatory focus is to ask them
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to rank statements regarding peoples' perceptions in the decision making process. For example, “When I see an opportunity for something I like, I get excited right away”. If this statement is ranked 4 or 5 points in the Likert scale that means the individual is more likely to remember events where the goal is followed by using eagerness approaches, thus he possesses promotion focus orientation. In the same way prevention focus is measured as well. For example, if the statement “I worry about making mistakes” is rated higher than 3 points, it means, that a person is more likely to remember events where the goal is followed by means of vigilance and possesses prevention focus orientation (Crowe and Higgins, 1997).
Individual’s cognitive biases
Hindsight bias occurs when respondents are tending to change their opinion about possible outcomes after a particular event occurs. For this reason, at the beginning of the questionnaire five difficult general knowledge questions were placed. Students had to choose one of two options and estimate the percentage level of them being right about choosing the correct answer. At the end of the questionnaire correct answers to the previous questions were given and it was asked to remember (without looking back) their estimated probability about how sure they were that their answer was correct. Since filling out the questionnaire takes approximately 20 minutes, respondents could not remember their first choice correctly, and in this way the level of their hindsight bias was measured. Questions were based on researches, being made by Podoynitsa et al (2010) and Bernstein et al (2011).
Overconfidence occurs when individuals fail to know limits of their knowledge. To measure overconfidence the procedure of Podoynitsa et al (2010) was followed. Similarly as for hindsight bias, another five difficult general knowledge questions were asked. Students had to choose one of two options – correct and incorrect – and specify how confident they are that the answer they chose is correct. Overconfidence was ranked on a 5 points scale, according to how sure respondents were with their answer when the answer appeared to be incorrect. Law of Small Numbers occurs when people make judgments depending on a basis of a small sample or trusting a small amount of information available. To measure the level of the small number bias individuals exhibit, two types of questions were asked: five 5 point Likert scale questions and four case questions. Most of the questions were taken from Simon et al (2010) and Tversky and Kahneman (1974) papers. Others were made following an example, to fit small number bias measurement. An example of the scale questions is: “When making
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strategic decisions, it is sufficient to ask the opinion of a few of my closest friends and colleagues”. Students were asked to rate statements according to how strongly they agreed with the statement. The higher the rating, the greater the law of small number bias they demonstrate. However, in the scenario questions there are wrong and right answers, where right answers are usually numbered as “1”. The more people deviate from the correct answer, the greater the law of small number bias they demonstrate.
Let’s consider one case question example. Suppose that a certain town is served by two hospitals. In the larger hospital about 45 babies are born each day, and in the smaller hospital about 15 babies are born each day. Usually, about 50 percent of all babies are boys. However, the exact percentage varies from day to day. Which hospital, do you think, for a period of 1 year recorded more days on which more than 60 percent of the babies born were boys? Most respondents answered that the probability of obtaining more than 60 percent boys is the same in both hospitals, probably because these events are described by the same statistic and are therefore equally representative of the general population. Nevertheless, correct answer should be that the expected number of days on which more than 60 percent of the babies are boys is much greater in the small hospital than in the large one, because a large sample is less likely to stray from 50 percent (Tversky and Kahneman, 1974).
Base rate regression occurs when individuals making judgments depend on irrelevant information instead of available statistical information. To measure the level of base rate regression a biased individual exhibits, four hypothetical scenarios were presented: two were based on student life and two were more general questions. At the beginning the relevant statistical information was given, followed by the irrelevant information. At the end there was a question referring to the previously given statistical information. For example, it was said, that statistics show that about 60% of first year students do not pass their exams the first time and that Tom is a first year student of economics, which is relevant statistical information. After that, some unnecessary information about Tom was given: “He is tall, handsome guy, who gets a lot of attention from girls. Yesterday he told his friend John, that he really likes Nathalie, a girl from their class.” At the end of the scenario question the individual was asked to estimate the probability that Tom will pass his exam the first time this year. Without paying attention to the irrelevant information, correct answer is obvious – if there are 100% students in the class, from who 60% fail on the exam first time, therefore, the probability that Tom will pass is 40%. However, students with high levels of base rate bias are not able to ignore
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irrelevant information which influences their decision making abilities. The more they deviate, the higher the level of the base rate regression they demonstrate.
Illusion of control occurs when individuals erroneously perceive certain events being within their control. In order to calculate the level of illusion of control bias an individual possesses, five questions from Zuckerman et al (1996) were chosen. Students were asked to rate statements, according to how well they describe their perception of reality. The lower the score, the higher the illusion of control bias the individual demonstrates.
Illusory correlation occurs when individuals see relationships between two events when no actual relationships exist. To measure illusory correlation bias the procedure of Tversky and Kahneman (1974) was followed. Students were asked to rate five statements, representing illusory correlation, according to how strongly they agree with them. Higher scores indicate higher illusory correlation the individual exhibit.
Regression fallacy occurs when there are attributed non existing causes for a certain outcomes. To measure this bias five 5 point Likert scale questions were modified following the Podoynitsa et al (2010) procedure. Students were asked to rate statements according to how strongly they agree with them. For example: “After some road accident emergence, installing speed cameras on a road highly improves road safety”. The higher this statement is rated, the higher the level of regression fallacy is demonstrated, because the number of accidents just moved closer to the mean, which means, that installing speed cameras was not necessary the main cause of increased road safety.
Dependent variable
Individual’s risk-taking propensity
With reference to Weber et al (2002) a psychometric scale was used that assesses risk taking in five content domains: financial decisions (the investing part was eliminated since it is not highly related to students), health and safety, recreational, ethical, and social decisions. This scale was chosen in order to examine all possible areas in case individuals are more likely to take risks in one domain than in the other. Students were asked to indicate one’s likelihood of engaging in each activity or behavior in a scale from 1 to 5, were 1 means very unlikely and 5 – very likely. The scale consists of two questions for each domain. Higher numbers in a rating scale indicate higher individuals’ risk taking propensity. For example, students were asked
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how likely it is for them to engage into an activity, such as cheating on an exam. If answer 4 or 5 were chosen, it indicated a high or very high risk taking propensity.
The second part of the questions measuring risk taking propensity is based on four different scenarios: two student related and two general. Students received questions with two possible options: one certain but with lower added value and one risky, though, with some predictable value. If students chose certain option, they were assigned to the group of risk averse individuals, and opposite, if they chose uncertain option, they were assigned to risk seeking individuals group.
To strengthen the measurement of risk taking propensity, an additional scenario was added, based on the certainty equivalent approach (Podoynitsa et al, 2010). In this question students had to choose between one certain option – receiving $900 for sure – and nine riskier options. The more certain option they chose, the lower their risk taking propensity is.
ANALYSIS AND RESULTS
Confirmatory factor analysis was made using Lisrel 8.8 statistical software. During the process it was found that multicollinearity between questions, representing the same variable, does not exist. The most probable cause of that could be respondents’ incomplete concentration while answering the questionnaire. Because of this reason, answers were chosen without deep insight into questions leading to low reliability between questions, representing the same variable. Having potential problems regarding a lack of cohesion between questions reflecting the same construct, multiple linear regression using one-item construct, was chosen as a tool to predict the dependent variable, influencing risk taking propensity. Nine questions, representing each variable were chosen, depending on its correlation with risk taking propensity. The main descriptive statistics of the chosen questions and correlations between the variables might be seen in the Table 2.
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Pearson Correlation
Mean Std.
Deviation risk10 hind500 Br1 PF13 PF24 IoC2 Ic5 rf5 sm2x
risk10 1,98 1,185 1,000 hind500 ,68 1,049 ,118 1,000 Br1 4,71 1,675 ,056 ,183 1,000 PF13 2,16 ,974 -,285 ,227 -,043 1,000 PF24 2,35 1,162 ,257 -,093 -,105 -,241 1,000 IoC2 3,24 1,065 ,184 ,004 ,206 ,169 -,029 1,000 Ic5 3,28 ,983 -,096 -,039 -,057 ,075 ,068 -,041 1,000 rf5 2,44 ,906 ,154 -,241 -,009 -,082 ,045 ,016 ,208 1,000 sm2x 1,80 ,910 ,051 -,080 ,094 ,118 ,034 ,061 ,184 ,237 1,000 over5 ,81 1,393 ,387 -,025 ,160 ,014 ,122 -,026 -,108 ,075 -,011
Table 2: Descriptive statistics and Correlations
Unstandardized Coefficients
Standardized Coefficients
Model B Std. Error Beta t Sig.
(Constant) 1,160 ,690 1,682 ,097 overconfidence ,334 ,079 ,393 4,218 ,000 Illusion of control ,306 ,104 ,275 2,934 ,004 Prevention focus ,139 ,096 ,136 1,437 ,155 Promotion focus -,449 ,121 -,369 -3,721 ,000 hindsight ,323 ,109 ,286 2,955 ,004 Regression fallacy ,188 ,126 ,144 1,489 ,141 Small numbers ,118 ,123 ,090 ,952 ,344 Illusionary corr. -,080 ,113 -,067 -,709 ,481 Base rate regression -,091 ,069 -,128 -1,318 ,192
Dependent Variable: risk taking
Table 3: Coefficients
Regression analysis was used to test direct effects of biases towards risk taking propensity. The best model was chosen where adjusted R square is 0,331 (F=5.6, p<000.1) indicating, that approximately thirty percent of the variation in the response variable can be explained by the explanatory variable.
A significant relationship between promotion focus and risk taking propensity can be seen in Table 3, though, contrary to the Hypothesis 1, it is negative (β=-0.369, p<0.0001).
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Hypothesis 2 concerns the relationship between prevention focus and risk taking propensity. However, contradictory to the expectations, the relationship between these two variables is insignificant.
Hypothesis 3 explored the effects of biases, primarily originating from the representativeness heuristic, on risk taking propensity. The four biases, which are primarily originating from the representativeness heuristic, are base rate regression, illusion of control, law of small numbers and regression fallacy (Tversky and Kahneman, 1974). In the model it can be seen, that base rate regression, law of small numbers and regression fallacy are insignificant, while illusion of control has a positive effect on risk taking propensity (β=0,275, p<0.005). Thus, it can be concluded that Hypothesis 3 is partly confirmed.
Hypothesis 4 suggests a positive relationship between risk taking propensity and biases, primarily originating from the availability heuristic. The three biases, which are primarily originating from the availability heuristic, are hindsight, overconfidence and illusory correlation (Tversky and Kahneman, 1974). Results show, that hindsight bias (β=0.286, p<0.005) and overconfidence (β=0.393, p<0.0001) have a positive impact on risk talking propensity. Contrary to Hypothesis 4, the relationship between illusionary correlation and risk taking propensity is insignificant.
XX DISCUSSION
MAJOR RESEARCH FINDINGS AND THEORETICAL IMPLICATIONS
The study aims to identify determinants influencing risk taking propensity. This study’s research questions asked: 1) how does individual’s regulatory focus affect risk taking propensity of the individuals 2) how do individual’s cognitive biases affect individual’s risk taking propensity. The results, as summarized in Figure 1, cautiously suggest the answer, nevertheless, let’s discuss it separately.
First of all, findings show that the promotion focus has a negative effect on risk taking propensity. There are two possible reasons, explaining this kind of outcome. To begin with, for people who are focused on reaching many goals in their lives, it is natural that a lot of the efforts fail. Though, without learning from their mistakes, they will be doomed to make them again. If individuals want to be successful, they cannot afford to fail in every step undertaken. For this reason, individuals start thinking more carefully about their actions and possible
Availability heuristic. Statistically significant biases: hindsight (+) overconfidence (+) Representativeness heuristic. Statistically significant biases: illusion of control (+) Promotion focus (-‐) Risk-‐taking propensity
Figure 2: Variables impact on the risk taking propensity
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outcomes, which results in a lower risk taking propensity. Another possible explanation may be that people, whose promotion focus is dominant, are not just eager to reach their personal goals and aspirations, but also have some other characteristics, which lead to lower risk taking tendency. It is possible that, individuals’ decisions are based on the existing circumstances and actions of others or, as Kahneman and Tversky (1982) have found, individuals do not always act rationally toward uncertainty. In other words, although, they are looking for every opportunity possible, their life experience or another mediators, not tested in this study, has indirect effect on risk taking propensity.
Secondly, the study found that there are two biases, preliminary originating from availability heuristic, which have positive effect on risk taking propensity. Overconfidence has the strongest positive influence on risk taking propensity. Individuals, who possess higher levels of overconfidence, are more likely to make riskier decisions. The possible reason, explaining overconfidence positive influence on risk taking is due to individuals’ greater believe in the accuracy of their assumptions (Simon et al., 1999). In a situation, where individuals are certain that their assumptions will lead to optimistic conclusions, they tend to act, although, their assumptions may not be correct.
Moreover, hindsight bias has positive effect on risk taking propensity as well. The explanation for that would be individuals’ wrong impression about the factors which lead to success or failure. Since they do not realize their predictions being inaccurate, they fail to recognize real causes for a certain outcomes. In view of the fact that individuals are not fully aware of which aspects influence success or failure, they are tending to make more risky decisions. It is interesting to note, that according to Bernstain et al. (2011), hindsight bias persists through life following a U-shaped function. This means that this bias decreases at older children and young adults and sharply increases at older age. Since our respondents were students, so young adults, they may not possess such strong hindsight bias. However, increase of a lower level of hindsight bias has a same positive effect on risk taking propensity as well.
What is more, the study found that illusion of control bias, preliminary originating from representativeness heuristic, also has positive effect on risk taking propensity. Possible explanation for that could be that individuals, who are biased with illusion of control, so are confused between their skill and chance situations, are more motivated to persist at tasks when they might otherwise give up. By continuing their actions, which may or may not give positive outcomes, they engage in riskier situations. Moreover, in a favor of this study results, Fenton-O'Creevy et al. (2003) as well as Gollwittzer and Kinney (1989) argue, that illusions
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of control may cause insensitivity to feedback, slow down learning and influence toward greater objective risk taking, since subjective risk will be reduced by illusion of control. Finally, contrary to the expectations, it was found that prevention focus regulatory orientation and regression fallacy, law of small numbers, illusionary correlation and base rate regression heuristics do not have statistically significant influence on risk taking propensity. These results may be caused by unreliable database. Since during confirmatory factor analysis there was no multicollinearity found between questions, representing the same variable, it might be assumed that database is undependable.
LIMITATIONS AND FUTURE RESEARCH
Limitation in this study occurs in the data collection process. Although the questionnaire was made according to already published studies and the student sample was selected randomly, however there was no multicollinearity found between the questions construct. It is very likely that respondents were not accurate enough filling out the questionnaire. This was the most probable cause of the non-existing multicollinearity and low reliability between questions, representing the same variable.
For the future studies it would be important to motivate respondents to spend more time by reading and filling out the questionnaire in the more honest way. Questionnaire could be made in the native language of respondents. Furthermore, better selection of students could be done, which would also help to control number of different nationalities between respondents, avoiding cultural difference bias. Moreover, it would be helpful to pre-test the questionnaire thoroughly, by asking students to indicate questions, which were harder to understand. What is more, it would be interesting to explore, if individual risk taking propensity differs from the team’s risk taking propensity, by investigating factors, such as students’ teams involvement in a stimulated risk-taking task. It could be possible, that individual being a part of a team may tend to make riskier decisions as it could make him feel safer since in case of a failure he could share responsibility for an actions undertaken. It could be tested by grouping students into units of 3-5 people and involving them into simulated situations, requiring to make risky decisions.
XXIII References
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XXVI APPENDIX 1
QUESTIONNAIRE MAP
I. Individual’s regulatory orientation. Taken from Haws et al., 2010 Promotion Focus [PF1]
1. When it comes to achieving things that are important to me, I find that I don’t perform as well as I would ideally like to do. (R)
2. I feel like I have made progress toward being successful in my life.
3. (*) When I see an opportunity for something I like, I get excited right away. 4. I frequently imagine how I will achieve my hopes and aspirations.
5. I see myself as someone who is primarily striving to reach my “ideal self”—to fulfill my hopes, wishes, and aspirations.
Prevention Focus [PF2]
1. I usually obeyed rules and regulations that were established by my parents. 2. Not being careful enough has gotten me into trouble at times. (R)
3. I worry about making mistakes.
4. (*) I frequently think about how I can prevent failures in my life.
5. I see myself as someone who is primarily striving to become the self I “ought” to be— fulfill my duties, responsibilities and obligations.
II. Individual’s cognitive biases
Hindsight. Taken from Podoynitsyna et al. (2010) and Bernstein et al. (2011), new items (at the end of the questionnaire)
Below there are questions representing your confidence on your answers to previous questions. Please mark the percentage representing the level of your confidence in your answer without turning the pages back and changing any answer.
Hind1. Earlier we asked you which country (Canada or New Zealand) has a higher percentage of entrepreneurs. Let’s assume that the correct answer is New Zealand. Knowing this new information please answer the following question:
If your answer was New Zealand, how confident were you this is correct answer? If your answer was Canada, how confident were you this is correct answer? Hind2. Earlier we asked you which city is farther away from Seattle (London or Beijing). Let’s assume that the correct answer is Beijing. Knowing this new information please answer the following question:
If your answer was Beijing, how confident were you this is correct answer? If your answer was London, how confident were you this is correct answer? Hind3. Earlier we asked how many days can cockroach live without a head (9 or 5). Let’s assume that the correct answer is 9. Knowing this new information please answer the following question:
If your answer was 9, how confident were you this is correct answer? If your answer was 5, how confident were you this is correct answer?
Hind4. Earlier we asked how many teeth does the mosquito have (20 or 47). Let’s assume that the correct answer is 47. Knowing this new information please answer the following question:
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If your answer was 47, how confident were you this is correct answer? If your answer was 20, how confident were you this is correct answer?
Hind5. (*) Earlier we asked you which company was founded earlier (Nissan or Toyota). Let’s assume that the correct answer is Nissan. Knowing this new information please answer the following question:
If your answer was Nissan, how confident were you this is correct answer? If your answer was Toyota, how confident were you this is correct answer?
Coding algorithm: Let’s the questions in the beginning of the questionnaire be questions A, and questions at the end of the questionnaire be questions B.
If the answer is correct, then score = scoreB - scoreA, else score = scoreA - scoreB. Moreover, if score is negative, it is turned into 0, as then respondents are not seen as being hindsight, so changed their predictions to be more accurate.
Overconfidence. Taken from Klayman et al. (1999) and Bernstein et al. (2011).
Below are some challenging questions. One of the two possible answers is correct. Please work through the questions quickly and check the box with response that best represents your answer.
Over1. Which of these “tourist cities” has a warmer daily high temperature in July, on average: Istanbul or Rome?
Over2. Which of these U.S. presidents held office first: William Henry Harrison or Over3. Benjamin Harrison?
Over4. Which of these food items has more calories: Carrot or Cauliflower? Over5. (*) How many weeks are female dogs pregnant: 16 or 9?
Over6. Of these two “principal mountains of the world”, which is taller: Mt. McKinley or Kilimanjaro?
Coding algorithm: if answer is correct, then code=0, else code=score
Small numbers [SM]. New items, made following Podoynitsyna et al. (2010).
Sm1. When making strategic decisions, it is sufficient to ask the opinion of a few of my closest friends and colleagues.
Sm2. (*) When making important decisions in my life, I always use more than one source of information. (R)
Sm3. Preparing for the exams, I checked just questions from last two exams to imagine, what questions might be.
Sm4. Writing reports for the university I rely mostly on Wikipedia as a source.
Sm5. The apples on the top of the box look good. The entire box of apples must be good.
Scenario questions, taken from Tversky and Kahneman (1974),
http://primes.utm.edu/glossary/xpage/LawOfSmall.html and new items,
We present some hypothetical scenarios below. Please give your opinion for each of the scenarios.
Smcase1. A certain town is served by two hospitals. In the larger hospital about 45 babies are born each day, and in the smaller hospital about 15 babies are born each day. As
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you know, about 50 percent of all babies are boys. However, the exact percentage varies from day to day. Sometimes it may be higher than 50 percent, sometimes lower. For a period of 1 year, each hospital recorded the days on which more than 60 percent of the babies born were boys. Which hospital do you think recorded more such days?
Smcase2. Do you agree that every odd number is prime?
Smcase3. My roommate said her statistic class was hard, and the one I'm in is hard as well. All statistic classes must be hard.
Smcase4. Try to divide some first few primes by four. Which number, 1 or 3, do you think appears more often as the remainder?
Base rate regression. New items, made following Podoynitsyna et al. (2010).
We present some hypothetical scenarios below. Please give your opinion for each of the scenarios.
Br1. (*) Statistics show that about 60% of first year students do not pass their exams at the first time. Tom is a first year student of economics. He is tall, handsome guy, who gets a lot of attention from girls. Yesterday he told his friend John, that he really likes
Nathalie, a girl from their class. What is the estimated probability, that Tom will pass his exam at the first time this year?
Br2. Your friend Emma is looking for a new laptop. It is known, that 60% of the model she picked up requires major repairs after 3-4 years. After you left lecture, she called to let you know, that she looked at it once more and she really likes design and color of the computer. Moreover, her birthday is coming soon and it would be a great present for her. What is your estimated probability that Emma will need some major repairs after three or four years?
Br3. Ross is planning a trip to Istanbul in November. Statistics show that 40% of the days in November are cloudy and rainy in Istanbul. Ross just brought a travel book, new bag and few pairs of t-shirts for the trip. He is really excited and cannot wait to go there. What is your estimated probability that the days Ross will spend in Istanbul will be rainy and cloudy?
Br4. Marc is working as a junior accountant for already two years. Usually, around 70% of the employees gets promotion after their second year. Marc wants to get married and create a family, so to have stable job and good salary is really important for him. What is more, he just brought new very expensive car. What is your estimated probability that Marc will be promoted after this year?
Illusion of control [IoC]. Taken from Simon and Houghton (1999) and Zuckerman et al. (1996)
IoC1. There is no such thing as misfortune; everything what happens to us is the result of our own doing.
IoC2. (*) In each and every task, not finishing successfully reflects a lack of motivation. IoC3. Even if I do everything I am capable of, some people may not like me. (R)
IoC4. I can keep any friend from engaging in irresponsible behavior (e.g., taking drugs). IoC5. I don't always know when others deceive me. (R)
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Illusory correlation [Ic]. New items and questions taken from Podoynitsyna et al. (2010). Below we list some personality characteristics. Please circle the number next to each
statement that best represents your degree of disagreement or agreement, where 1 – strongly disagree, 2 – disagree, 3 – neutral, 4 – agree and 5 – strongly agree.
Ic1. Big business will often ruin the small ones
Ic2. Universities are more likely to license to big companies Ic3. Cats that are sprayed or neutered automatically gain weight Ic4. Guys are more likely to fail exams then the girls are
Ic5. (*) Sugar makes children hyperactive
Regression fallacy [rf]. New items, made following Podoynitsyna et al. (2010). Below we list some personality characteristics. Please circle the number next to each
statement that best represents your degree of disagreement or agreement, where 1 – strongly disagree, 2 – disagree, 3 – neutral, 4 – agree and 5 – strongly agree)
rf1. After visiting doctor patients feel much better than before. rf2. Punishing students is very effective in improving students' grades.
rf3. After some road accident emergence, installing speed cameras on roads highly improves road safety.
rf4. After hitting the gym, people get much more compliments about their appearance then they did before.
rf5. (*) If you feel lonely, after reading a book “How to make friends and influence people”, it is much more likely to make new friends.
III. Individual’s risk-taking propensity. Taken from Tversky and Kahneman (1974), Weber et al. (2002), Podoynitsyna et al. (2010) and new items.
For each of the following statements, please indicate your likelihood of engaging in each activity or behavior. Provide a rating from 1 to 5, using the following scale where 1 - Very unlikely, 2 – Unlikely, 3 - Not sure, 4 – Likely, 5 - Very likely
risk1. Defending an unpopular issue that you believe in at a social occasion. (S) risk2. Cheating on an exam. (E)
risk3. Disagreeing with your father on a major issue. (S) risk4. Gambling a week’s income at a casino. (G) risk5. Buying an illegal drug for your own use. (H) risk6. Trying out bungee jumping at least once. (Re) risk7. Shoplifting a small item (e.g. a lipstick or a pen). (E) risk8. Engaging in unprotected sex. (H)
risk9. Going down a ski run that is beyond your ability or closed. (Re)
risk10. (*) Betting a day’s income on the outcome of a sporting event (e.g. baseball, soccer, or football). (G)
We present different scenarios below. Assuming that given probabilities are accurate, what would you choose if you have to make a decision now without additional information? risk21. I would always choose the following scenario (please check one answer only): Receiving $900 for sure,
a 90% chance of winning $1,000 and 10% chance of winning nothing, a 80% chance of winning $1,125 and 20% chance of winning nothing,
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a 70% chance of winning $1,286 and 30% chance of winning nothing, a 60% chance of winning $1,500 and 40% chance of winning nothing, a 50% chance of winning $1,800 and 50% chance of winning nothing, a 40% chance of winning $2,250 and 60% chance of winning nothing, a 30% chance of winning $3,000 and 70% chance of winning nothing, a 20% chance of winning $4,500 and 80% chance of winning nothing, a 10% chance of winning $9,000 and 90% chance of winning nothing.
risk22. Imagine the situation that you fail one really hard exam at the first time and you have to take a reset. Unfortunately, the date of the resit is on the same day as your flight home or to the country you were planning to go for a long time. That means that you must leave the examination room 1,5hour after the beginning of the exam instead of 3hours.
Knowing, that this is your last change to pass the exam and last time you spend around 2hours writing it, what choice would you make:
You would cancel the flight and concentrate on your exam, or
You would try to do both: take the exam, leave the room earlier and take the plane
risk23. Your friends are going to try new extreme sport which you were interested for a long time as well, however you have a big test coming in few days, so not studying, any injury or catching cold may cause failing this test.
Knowing the situation, what would be your decision:
You would go with your friends and try to pass the test after You would stay at home and study for your exam
risk24. Imagine that your neighbor got highly contagious disease. Moreover, (s)he is asking you to bring her/him some medicine and to stay with the kids at least for few hours after their school.
Knowing that there is a change you can get sick as well, what would you choose: You would try to help sick neighbor as much as you can
You would say sorry, but you would not help her/him, thinking that it is too dangerous for you.
risk25. Imagine that friend you haven’t seen for a while is in town for two days and the only night (s)he has, (s)he wants to spend with you going out to the city. However, you know that tomorrow you have to wake up really early for the class, where you will be graded for participation.
What would be your choice for that night:
To meet your friend and tomorrow go to the class tired and sleepy
Stay at home and prepare for the class, take a good night sleep and tomorrow try to do your best
(*) Questions, taken into the model (R) Reversed scale questions (G) Financial decisions (H) Health and safety (Re) Recreational (E) Ethical
