PREVALENCE AND CORRELATIONS OF BIASES IN MANAGERIAL
DECISIONS: A CASE STUDY OF THAI MBA STUDENTS
Chinnawut Jedsadayanmeta1,*, Dolchai La-ornual2#
1
MBA in Business Modeling and Analysis, Mahidol University International College,
Mahidol University, Thailand
2
Business Administration, Mahidol University International College, Mahidol University,
Thailand
*e-mail: chinnawut.jed@student.mahidol.ac.th, #e-mail: dolchai.lar@mahidol.ac.th
Abstract
People often display judgmental or cognitive bias when making decisions, which can
result in suboptimal outcomes. For organizations and businesses, these biases may lead to
losses in profits and resources. In this study, we test 10 common cognitive biases for their
prevalence and correlations among 71 Thai MBA students with work experience. The results
show that overconfidence and regression to the mean are the two most prevalent biases. We
also find that participants who exhibit the conjunction fallacy bias are likely to display the
insensitivity to base rate bias. Our findings can help organizations and businesses design
corrective and preventive actions to minimize potentially adverse effects from these common
biases in managerial decisions.
Keywords: cognitive bias, managerial decisions, prevalence, correlation, Thailand
Introduction
We all make decisions every day. However, we do not always explore every detail
and evaluate all possibilities before arriving at a conclusion. Thus, our decision making
processes may not always yield optimal solutions. 1 Researchers have identified several biases
as the causes of suboptimal decision making. In particular, people generally use a process of
“natural assessment”2 to perceive or understand a message or an event by comparing and
estimating.
Many biases occur because of the underlying heuristics, which are mental shortcuts
that are derived from one’s experience or prior knowledge to help a person in making
decisions, solving problems, learning, and discovering new things. 3 These “shortcuts” save
the decision maker time and money, but, possibly, at the cost of accuracy and quality of the
decisions. 1 Although biases rarely lead to optimal decisions, they frequently lead to ‘good’
decisions. 4 And because of limited daily resources including time and effort, it is difficult, if
not impossible, for people to avoid using biases in their lives.
Biases occur in almost every field, including business. They occur in managerial
decisions in the form of performance evaluations, sales forecasts, investment choices, and
strategic interactions. Biases and heuristics can lead to both gains and losses in business. For
example, heuristics can help eliminate excessive information when screening new projects. 5
Conversely, flawed decisions can lead to losses to firms, such as when making decisions
under unsuitable circumstances, with inappropriate emotion, or under public scrutiny. 6
Losses can be in terms of money or opportunities. An overestimated inventory level can lead
to higher holding cost and the first impression of a job candidate lead to an ill-suited
employee.
Previous researchers have identified many biases in decision making. 10 common
ones studied in this paper are (1) ease of recall, (2) retrievability, (3) insensitivity to base
rates, (4) insensitivity to sample size, (5) misconceptions of chance, (6) regression to the
mean, (7) the conjunction fallacy, (8) anchoring, (9) conjunctive- and disjunctive-events, and
(10) overconfidence. And although these biases are well known and widely accepted by
researchers in the field of judgment and decision making, the authors of the current paper are
not aware of research that have investigated these biases comparatively. In particular, there
has been no study that has compared the prevalence of each bias and possible correlations
among them, especially in the context of managerial decisions. Furthermore, understanding
the behavior of local managers in a particular country should benefit foreign entities with
economic relations to it. This is particularly relevant for Thailand as the ASEAN Economic
Community (AEC) which it will be a part of, is planned to be officially established in 2015.
Common Biases
In general, a bias in decision making occur as a result of an underlying heuristic or
mental shortcut that are influenced by the decision maker’s existing experience or prior
knowledge. The three heuristics, which causes the 10 cognitive biases tested in this study, are
the availability, representativeness, and confirmation heuristics.
The first heuristic is availability, which is based on how easy people think of
something. It is a mental shortcut used in estimating probabilities of events by judging how
easy it is to come up with examples. 7 It rests on a presumption that if examples can be
recalled or retrieved easily, it must also be more probable. 8 Two biases emanating from the
availability heuristic that we examine in this paper are: ease of recall and retrievability.
The second heuristic is representativeness, which is based on how similar things are.
As put forth by Tversky and Kahneman, 9 it is “an assessment of the degree of
correspondence between a sample and a population, an instance and a category, an act and an
actor or, more generally, between an outcome and a model.” For example, if a person
experiences a particular event more often than another, that individual will think that the
former is more common than the latter. 9 Five biases emanating from the representativeness
heuristic that we test in the current study are: insensitivity to base rate, insensitivity to sample
size, misconception of chance, regression to the mean, and conjunction fallacy.
The third and final related heuristic is confirmation, which is based on how someone’s
beliefs measure up against new information. When people are presented with information that
is consistent with their beliefs, they are more likely to accept that information. 10 For
example, a person who believes that a stricter gun control in the United States is mandatory
will mostly agree to any studies or research pointing out to the same conclusion and will
dismiss or turn antagonistic toward information that says otherwise. 11 Three biases
emanating from the confirmation heuristic that we examine in this paper are: anchoring,
conjunctive and disjunctive events, and overconfidence.
Based on the three heuristics described above, the following are the 10 common
biases in decision making that are tested in this study.
Ease of recall
This bias occurs when an event that is more vivid, is covered more by the media, or
has happened recently is easier to recall than those that are not. 8 Consequentially, people
assign higher probabilities to such events than other events that are more difficult to recall.
For example, research has shown that organizations tend to buy natural disaster insurances
after they have experienced such an event and not before the event has actually occurred. 12- 13
Retrievability
This bias is based on how the human’s memory is structured and how the structure
affects the search process. In an experiment, Tversky and Kahneman 9 found that participants
estimated that the number of seven-letter words with the letter “n” in the sixth position is less
than seven-letter words with “ing” as an ending even though the latter is a subset of the
former. With respect to management, human resources managers, even without
discriminating, are more likely to hire the type of people that they are similar to than those
that they are not. 14
Insensitivity to base rate
In this bias, people ignore the base rate when they are given descriptive information,
no matter how irrelevant the information is. As an illustration, Kahneman and Tversky 15
found that giving participants an engineer-resembling description of a randomly selected
individual from a group highly influenced the judged probability of that person being an
engineer despite the different base rates in proportion of engineers in the group. From an
organizational perspective, new entrepreneurs spend more time thinking of success rather
than considering failures because they think that the base rate of failure is not applicable to
them. 16
Insensitivity to sample size
This bias occurs when people, using representativeness heuristic, incorrectly interpret
that every sample size provides the same value of information. 17 In particular, the researchers
showed that participants assess the likelihood of a particular event from a small sample size
to be similar to the actual proportion in the population. In an organization, the marketing
department usually plays on consumer’s insensitivity by giving numbers in percentage form
without disclosing the sample size in advertising messages. 10
Misconception of chance
In this bias, people assess the chance of a sequence to be random even when the
sequence is too short for it to be so. 15 As the result of getting a head of an earlier flip of a
coin has no effect on the current flip, so does prior performance has no influence on the
present one. 18 Not uncommonly, many fans and gamblers are susceptible to fallacy by
choosing to bet too heavily on the sports person who is perceived to be on a hot streak.
Regression to the mean
Regression to the mean is a concept many people ignore when they assess the
likelihood of extreme events and the next event. For example, a business or an employee who
performs really well this year cannot be expected to perform as well at the same level again
next year. 15, 7 However, in some extreme cases, regression to the mean is to be expected. 10
For example, if an employee with a historically low performance performs exceptionally well
in one year, the manager will not expect the employee to achieve the same high performance
again in the future.
Conjunction fallacy
This bias occurs when people violate the conjunction rule, which states that “the
probability of a conjunction, P(A&B), cannot exceed the probabilities of its constituents,
P(A) and P(B), because the extension (or the possibility set) of the conjunction is included in
the extension of its constituents.” 9 Yates and Carlson 19 explained that it is because of how
the vivid representativeness of such conjunctions that matches to the given description that
makes P(A&B) more probable than either P(A) or P(B).
Anchoring
This is a phenomenon that happens when people’s judgments are distorted by existing
or new information (an “anchor”), even if it is irrelevant, externaly given, or self-generated. 17
From an organizational perspective, employees face with anchoring when making estimations
of several issues, such as sales, inventory, and expense. Two explanations are given. First,
Epley and Gilovich 20 explain that, when the anchor is self-created, people make an
adjustment from an initial anchor by increasing or decreasing from it. In contrast, when the
anchor is externally given, people arrive at an estimate from thinking of or searching for
information that is related to the anchor. 21 The confirmation heuristic triggers this tendency
in selective searching. 22 This process is similar to how first impression influences a hiring
decision, 23 when racial stereotype rises out of how people perceive other people of the same
race to those they have met before, 24 or when judges’ sentencing decisions could be
influenced by anchors as irrelevant as a roll of dice. 25
Conjunctive- and disjunctive-events
This is the bias where people misjudge the probabilities of the two events. More
precisly, they overestimate the probability of events that must occur in conjunction with one
another (conjunctive), such as in project planning 26 and underestimate the probability of
events that must occur independently (disjunctive), such as landing at least a head one time in
10 coin flips. 17
Overconfidence
People can be too self-assured in their beliefs and judgements when facing with
moderately to extremely difficult questions. 27 Employees in an organisation, especially those
in higher level positions, can make a wrong strategic decision when estimating many business
numbers because they are too sure of their abilities. Bazerman and Moore 10 explain that,
because of confirmation heuristic, people find it easier to search for or generate confirming
evidences rather than disconfirming evidences. Affirmatively, Plous 28 state “No problem in
judgment and decision making is more prevalent and more potentially catastrophic than
overconfidence.” Thus, in order to prevent such bias, Griffin Dunning, and Ross 29 offer that
instead of finding information to support someone’s theory, finding contradictory information
can be more helpful.
Methodology
Participants
The study involved 71 participants who are Master of Business Administration
(MBA) students in international programs at Mahidol University and Thammasat University
in Thailand. The participants had an average work experience of more than 3 years and were
employed in entry- or mid-level management positions. Participants were given a class score
as an incentive to complete the test.
Design
There were 10 questions in the test, each corresponding to a bias presented in the
previous section. These questions were adapted from Bazerman, M.H. and Moore, D.A. 10
Judgement in Managerial Decision Making, 7th edition, New York: John Wiley & Sons.
Participant had to respond to all questions, presented in random order to minimize the
sequencing effect. The test was conducted in English.
Procedure
Participants were invited to join the research project in the class. All participants were
informed of their rights and had given their informed consents before being given the test.
Instructions were given of how to complete the test. The test was administered online in a
computer lab setting using an interactive questionnaire. No time limit was set for the test;
however, most participants finished the test under 40 minutes.
Once all the test results had been recorded, it was possible to determine the
prevalence and the correlation of each bias in managerial decisions. Statistical tests are
performed to determine the significance of the results.
Results
Prevalence
100%
100%
90%
90%
83%
82%
83%
76%
80%
70%
60%
50%
49%
45%
40%
30%
27%
20%
20%
10%
0%
Figure 1. Prevalence of the common biases among Thai managers with the histogram displaying
percentage of participants exhibiting each type of bias.
Performing a Pearson’s chi-squared test, we find a significant violation of the biases
2(9, N = 71) = 222.02, p < .001. The lowest number of violated biases is two while the
highest number is nine, with a mean of 6.55.
The most prevalent bias (Fig. 1) in Thailand is overconfidence. The test shows a
100% violation. The second most violated bias is the regression to the mean bias.
The conjunction fallacy and conjunctive- and disjunctive-events rank as the third and
the fourth most common biases. For the conjunction fallacy, this shows a similar result as
concluded by Tversky and Kahneman. 9 Insensitivity to base rate and insensitivity to sample
size come fifth and sixth. Participants violate them by responding to the questions with the
biased answers. These two biases are both emanated from the same heuristic. The
representativeness heuristic causes people to ignore background information-the base rate-in
favor of specific information and incorrectly estimate the probabilities when dealing with
sample sizes.
In contrast, the least common biases in Thailand are, in order of decreasing
prevalence, retrievability, ease of recall, misconception of chance, and anchoring.
For the retrievability bias and the ease of recall, these two phenomena are triggered by
the availability heuristic, whereby the instances that are easily retrieved or the events that are
easier to recall caused the participants to believe that they are more common. Most
participants answer correctly in the test of misconception of chance and they rarely display
the anchoring bias.
Correlation
A Pearson’s correlation test is performed to determine the correlation of each bias
with one another. Table 1 displays the correlation coefficient of each pair of biases.
Table 1. Correlation matrix for the prevalence of biases in Thailand
Bias
1
2
3
4
5
6
7
8
9
10
1
1.00
0.01
-0.01
-0.09
-0.23*
0.11
0.03
-0.02
0.11
N/A
2
3
4
5
6
7
8
9
10
1.00
-0.04
0.29**
0.10
0.23*
-0.08
-0.06
-0.08
N/A
1.00
-0.18
-0.04
-0.03
0.37***
-0.13
0.08
N/A
1.00
-0.03
0.15
0.01
0.28**
-0.08
N/A
1.00
0.09
0.10
-0.14
0.1
N/A
1.00
0.23*
-0.07
0.1
N/A
1.00
-0.15
0.10
N/A
1.00
0.13
N/A
1.00
N/A
1.00
Correlation is significant at the 0.01 level (two-tailed)
Correlation is significant at the 0.05 level (two-tailed)
*
Correlation is significant at the 0.10 level (two-tailed)
Ease of recall
6. Regression to the mean
Retrievability
7. The conjunction fallacy
Insensitivity to base rate
8. Anchoring
Insensitivity to sample size
9. Conjunctive- and disjunctive-events
Misconception of chance
10. Overconfidence
Notes:
***
**
1.
2.
3.
4.
5.
The pair that shows the strongest significant level is the conjunction fallacy and
insensitivity to base rate, r(69) = 0.37, p < .01 with a moderate positive relationship: for those
who have the conjunction fallacy, they are moderately likely to have insensitivity to base rate.
The conjunction fallacy also displays a weak positive relationship with the regression to the
mean bias, r(69) = 0.23, p < .10, but to a small significant degree. Since all of these three
biases are emanated from the representativeness heuristic, they are likely to occur at the same
time. In order to avoid being distorted by representativeness heuristic, it is important to avoid
giving too much weight to only one specific piece of information and it is recommended to
consider other information that may be relevant as well. 30 For an organization, it is possible
to use teamwork to help reduce the biases by using brainstorming techniques to come up with
more cases and ideas that are not conventionally representative of the case at hands.
In addition, to a lesser significance, anchoring has a weak positive relationship with
insensitivity to sample size, r(69) = 0.28, p < .05. The former bias is emanated from
confirmation heuristic, while the latter comes from the representativeness heuristic. This
reveals that more than one heuristic can happen at any given time in decision-making
processes. 10 To reduce confirmation heuristic, organizations benefit more when employees
try to find disconfirming evidences when making managerial decision rather than conforming
evidences.
Lastly, the last three significant correlations involve at least one availability heuristic.
Retrievability has a weak positive relationship with insensitivity to sample size, r(69) = 0.29,
p < .05, and regression to the mean, r(69) = 0.23, p < .10. Conversely, ease of recall has a
weak negative relationship with misconception of chance r(69) = -0.23, p < .10: for those
who exhibit ease of recall, they are less likely to have misconception of chance. Availability
heuristic can be avoided or reduced by understanding what information is truly relevant and
not what information is readily or easily available at hands. 30 Again, brainstorming can help
in this case by allowing more ideas to surface before making any decisions.
With this result, the question for organizations now moves from what biases are there
to how to reduce those biases. The first step in dealing with biases is to acknowledge that
they exist. A strong corporate culture can help raise awareness that biases are naturally
occurred and it is easier to see biases in others than in oneself. In this case, using a
management concept called “devil’s advocate”-by letting someone takes a strong stand in a
particular belief and engages other employees in discussion and constructive argument-will
help reducing the biases.
In addition, some biases can be alleviated by questioning decision-makers frequently
to avoid major business errors based on short-sighted decisions. Nonetheless, some counterintuitive biases are harder to avoid. In this instance, an objective third-party, such as a
management-consulting firm, can provide insights into decision-making processes and
provide new ideas that are not influenced by the organization’s past experience.
Finally, as a general recommendation, organizations should have contingency plans or
back-up plans to prepare for any unanticipated events and to help the organizations recover
faster if a catastrophic event occurs. In an effort to reduce biases, organizations have to
consider the cost of how much to spend to achieve the goal, but achieving so can be
something that worth the effort.
Discussion and Conclusion
This paper comparatively tests 10 common biases in managerial decision making by
examining the prevalence and correlations of these biases. We find that overconfidence and
regression to the mean are the most common biases among Thai managers. We also find that
people who are susceptible to the conjunction fallacy are also prone to be insensitive to the
base rate. This latter finding may be because both biases are due to the same
representativeness heuristic.
There are certain limitations to the findings in this paper, however. First, because we
specifically studied decision making among Thai managers, the results may not pertain to
business people of other nations. This may simply be due to differences in culture, tradition,
and general way of thinking. Thus, it may be beneficial for future studies to examine the
behavior of managers from other nations.
The second limitation is that this paper is based on 10 specific cognitive biases. And
although these biases are among the most common in decision making, the list is not
exhaustive. Thus, perhaps other biases, including culture or nationality related ones, could be
identified and tested in further research.
The third limitation of this paper is that it only examines decision making of
individuals. People in organizations often make collective decisions. This could be
particularly true due to the collectivism nature of Asian cultures. Therefore, studying how
participants decide as a group may yield different results. And because research of biases in a
group decision making is scarce, exploration in this area may provide valuable contributions
to the field of judgment and decision making.
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