INSENSITIVITY TO PRIOR PROBABILITY BIAS IN OPERATIONS MANAGEMENT CONTEXT

INSENSITIVITY TO PRIOR PROBABILITY BIAS IN OPERATIONS MANAGEMENT CONTEXT Mohammed AlKhars *, Robert Pavur, and Nicholas Evangelopoulos College of Business University of North Texas Denton, TX 76203-5249 940 565-3107 Mohammed.alkhars@unt.edu Robert.Pavur@unt.edu Nick.Evangelopoulos@unt.edu ABSTRACT Behavioral operations management (BOM) is a relatively new stream line of research that have emerged for the last 2 decades. Its primary concern is to put the true human behavior into consideration when conducting research. BOM is divided into 3 categories: cognitive, social and cultural. In this paper, the focus is on the first category namely cognitive category. Since managers sometimes use some heuristics in their decisions making, they may commit cognitive biases. The cognitive bias that is discussed in this paper is called Insensitivity to Prior Probability bias. A scenario, called Restaurant Scenario, has been developed to study this bias. The survey was distributed to students in a business college. There were two groups of students. The first one received the survey without training about this bias and the second group received training about it. The results show that many students chose the biased decision. Moreover, the group which received training made less biased decision compared with the group which received no training. Finally, the study shows that delayed gratification can be used to partially predict this cognitive bias. *Author for correspondence -411-

INTRODUCTION Behavioral Operations management (BOM) gained popularity in the last few years. The main theme in BOM is to explicitly consider human behavior in OM models. Traditional OM models use a simplified set of human behavior assumptions. For example, people are assumed to be deterministic and predictable. Therefore, the dynamic nature of humans is not taken into account. Another assumption is that OM models usually deal with organizations objects such as machines, materials, cars and trucks (Boudreau et al., 2003). These models ignore the humans who operate these objects. Because OM models often overlook people, it is estimated that the models developed in OM literature are applied 50% of the time (Loch and Wu 2007). In order to increase the practicality of OM models, scholars in OM have advocated research in BOM (Gino and Pisano 2008; Bendoly et al. 2010). BOM is defined as OM is concerned with the study of the design and management of transformation processes in manufacturing and service organizations, building mathematical theory of the phenomena of interest and testing the theory with field data (derived from surveys, databases, experiments, comparative case studies, ethnographic observations, etc). Behavioral Operations Management is a multi-disciplinary branch of OM that explicitly considers the effects of human behavior in process performance, influenced by cognitive biases, social preferences, and cultural norms (Loch and Wu 2007). This definition classifies BOM into 3 categories: cognitive biases, social preferences and cultural norms. It can be inferred that the scope of BOM is very wide and deep and research can be performed in different dimensions and contexts to better understand how humans take decisions related to OM. The objective of this paper is to develop a scenario that shows how the Insensitivity to Prior Probability cognitive bias can arise in decision-making in an OM context. LITERATURE REVIEW Some scholars in decision-making have realized that many people take biased decisions in business contexts. Instead of focusing their attention on how people should take their decisions, the focus of these scholars is how people actually make their decisions. Tversky and Kahneman (1974) are considered pioneers in this field. They conducted a series of experiments especially in gambling to help them understand how people take their decisions. They observed that people usually use certain heuristics in their decision-making process. The use of such heuristics may lead to some cognitive biases. A heuristic is defined as a rule of thumb used by people to make decisions. A cognitive bias is defined as an observed systematic deviation in decision making. Tversky and Kahneman (1974) identified 3 heuristics usually used to make decisions. These heuristics are representativeness, availability and anchoring. In this paper, only representativeness heuristic will be discussed. The representativeness heuristic is used to answer a question of the form what is the probability that event A belongs to category B. In such a case, the decision maker may use the representativeness heuristic to solve this problem. If event A highly represents category B, the -412-

decision maker usually assigns high probability to event A. Conversely, if event A is not highly representative of category B, the decision maker usually assigns a low probability to event A. The use of representativeness heuristic may lead to 6 cognitive biases: 1. Insensitivity to prior probability of outcomes 2. Insensitivity to sample size 3. Misconception of chance 4. Insensitivity to predictability 5. The illusion of validity 6. Misconception of regression In this paper, the first cognitive bias will be considered. Insensitivity to Prior Probability of Outcomes This bias usually occurs when people are asked to estimate the probability of an outcome. In this case, the right way is to consider the prior probability of such event. However, some people would use representativeness heuristic to estimate this probability and ignore the prior probability in their estimation. A classic example is given by Tversky and Kahneman (1974): Steve is very shy and withdrawn, invariably helpful, but with little interest in people, or in the world of reality. A meek and tidy soul, he has a need for order and structure, and a passion for detail. People are asked whether Steve is more probably a librarian or a farmer. Since the description of Steve is a stereotype of a librarian, many people would consider Steve to represent a librarian more than a farmer. However, a key piece of information that should be used in this example is that there are 20 farmers for each 1 librarian. So, it is more probable that Steve is a farmer and not a librarian. Debiasing Strategies Cognitive biases are strongly imbedded in the human mind. It is difficult to completely remove such cognitive biases. However, literature shows that the negative impact of cognitive biases could be reduced through the use of debiasing strategies (Kaufmann, Michel and Carter 2009). Debiasing strategies are defined as the approaches and sets of actions aimed at reducing the detrimental influence of decision biases and as such to enhance the rationality and effectiveness of decisions (Kaufmann, Michel and Carter 2009). Training is considered one effective strategy to counteract the negative impact of cognitive biases. By making people aware of the existence of cognitive biases, people may try to avoid committing such bias. Since OM practitioners would improve the quality of their decisions with experience, the objective of the training is to make an effective decision more effective. Delayed Gratification Literature shows people who are patient tend to think more about their decisions and therefore they usually take informed ones. Conversely, people who are less patient usually use their intuition and therefore may take biased decisions. A good instrument to measure the patience of the person is to measure his or her delayed gratification. Those who delay their immediate gratification hoping to -413-

get more reward in the future tend to score high in intelligent tests such as IQ or SAT math. Therefore, it is expected that those people will take less biased decisions. Fredrick (2005) developed 17 questions measuring the person s level of delayed gratification. They include monetary, massage, tooth pulled, pay of overnight shipping, level of impulsivity, etc. In this paper, only monetary questions will be used to measure the delayed gratification level of the decision maker. RESEARCH METHOD The objective of this paper is to examine the impact of awareness and delayed gratification on making biased decision. So, experimental design will be used. One group will be presented with a scenario without providing awareness of the cognitive bias. The second group will be provided the scenario with awareness of the cognitive bias. Moreover, the decision maker s level of patience would be assessed using delayed gratification questions. Scenario Used in the Experiment In order to run this experiment, a scenario called Restaurant Scenario has been developed to urge the respondent to use the representativeness heuristic to take a decisions about the cause of a problem. The scenario has been designed to see if respondents would commit the insensitivity to prior probability of outcomes. The scenario is described as: ABC is a chain of buffet-style restaurants. Assume you are the new assistant store manager. Part of your duties is to maintain food safety procedures. The restaurant offers a soup bar, with six different types of soup available to the customers. You are aware that, according to the U.S. Department of Agriculture, thousands of deaths and millions of illnesses each year are directly linked to foodborne bacteria and other microorganisms. To control bacteria growth in your soups, it is important to keep their temperatures outside of the so-called danger zone, a range of temperatures from 40 to 140 0 F (5 to 60 0 C). Keeping soups at a safe temperature can be challenging, since they need to be heated when they are cooked, chilled when they are stored, and reheated when they are about to be consumed by the customers. Therefore, soups pass through the danger zone twice. Throughout the day, soups are stored in the refrigerator inside plastic bags. Four times a day, cold plastic bags are opened and soup is quickly heated on a stove. When offered to the customers, the six types of soup are kept warm inside six metal containers (bain-maries). Soup temperature at ABC restaurants is monitored every half hour during the period 11:30am 10pm, for a total of 22 measurements per day, which are entered into a soup temperature log. One morning, as you review the previous day s soup temperature log, you are puzzled and concerned by a few temperature entries that were around 120 0 F (49 0 C). When this problem occurs, the most likely cause is human error related to the handling of the refrigerator (e.g. the refrigeration temperature setting is too cold) or the stove (e.g. the heating temperature setting is not hot enough). While refrigerator problems generally occur six times more frequently than stove problems, you can recall many recent instances when the soup temperature was around 120 0 F toward the end -414-

of the day and the cause was the stove. When this type of problem can be traced to the refrigerator, about two-thirds of the time the problem occurs toward the beginning of the day, and only about one third of the times the problem occurs toward the end of the day. When the stove causes the problem, the problem tends to occur almost exclusively toward the end of the day. In fact, your records verify that, among the 12 occurrences of a temperature problem caused by the stove in the past six months, all 12 (100%) occurred toward the end of the day. Looking at the temperature log, you see that the problem this time occurred toward the end of the day. You now need to establish the most likely cause and take specific action. Q. Given that the problem occurred toward the end of the day, what is the most likely cause of the low temperature in soups? 1. The refrigerator 2. The stove In order to solve this problem, it is expected that many respondents will choose the stove as the most likely cause of the problem. Since the problem occurred toward the end of the day and as the scenario states if the stove is the cause, then the problem exclusively occurs toward the end of the day. However, the right way is to consider the prior probability of the outcomes. Since the refrigerator cause the problem 6 times more than the stove and one third of the time it occurs toward the end of the day, then the refrigerator causes the problem two times more often than the stove if the problem occurs toward the end of the day. Training One group will be presented the scenario without warning and the other group will be given the warning. The warning given for the second group is shown below: As you consider your choice between the refrigerator and the stove, please note that such choices are sensitive to a well-known cognitive bias, called Insensitivity to prior probability of outcomes. In this bias, the decision maker will jump to an intuitive choice after recognizing a familiar situation, without properly assessing an underlying probability. For example, suppose they give you a person s description as follows: Steve is very shy and withdrawn, invariably helpful, but with little interest in people, or in the world of reality. A meek and tidy soul, he has a need for order and structure, and a passion for detail. Then they ask you: is Steve more likely to be a farmer or a librarian? You will be tempted to select librarian, due to the resemblance of the description to a stereotypical librarian. However, there are many more farmers than there are librarians. Therefore, the description is actually more likely to correspond to a farmer, even though the percentage of people who fit the description is a minority among farmers. -415-

Delayed Gratification There are 9 questions aiming to measure the delayed gratification of respondents. The 9 questions will be given for all respondents. These 9 questions are Q. How likely are you to agree with each of the following statements? Very Likely A Equally likely Very Likely B A or B 1. Receive (A) $3400 this month or [1] [2] [3] [4] [5] [6] [7] (B)$3800 next month 2. Receive (A) $100 now or [1] [2] [3] [4] [5] [6] [7] (B) $140 next year 3. Receive (A) $100 now or [1] [2] [3] [4] [5] [6] [7] (B) $1100 in 10 years 4. Receive (A) $9 now or [1] [2] [3] [4] [5] [6] [7] (B) $100 in 10 years 5. Receive (A) $40 immediately or [1] [2] [3] [4] [5] [6] [7] (B) $1000 in 10 years 6. Receive (A) $100 now or [1] [2] [3] [4] [5] [6] [7] (B) $20 every year for 7 years 7. Receive (A) $400 now or [1] [2] [3] [4] [5] [6] [7] (B) $100 every year for 10 years 8. Receive (A) $1000 now or [1] [2] [3] [4] [5] [6] [7] (B) $100 every year for 25 years 9. Lose (A) $1000 this year or [1] [2] [3] [4] [5] [6] [7] (B) $2000 next year ANALYSIS AND DISCUSSION The survey was distributed to students studying at University of North Texas (UNT). 211 students participated in this survey. Tables 1 and 2 show age distribution and gender distribution of the sample. -416-

Table 1. Age distribution of sample Age 18-20 21-25 26-35 36-50 51-older Missing Total Group No. 27 145 34 3 1 1 211 % 12.80 68.72 16.11 1.42 0.47 0.47 99.99 The majority of respondents are in the range 18-35 years. This sums up to 98% of the respondents. The percentages sum up to 99.99% because of rounding error. Table 2. Gender distribution of sample Gender Male Female Missing Total No. 123 83 5 211 % 58.29 39.34 2.37 100.00 Table 3 shows the results of committing the Insensitivity to Prior Probability bias: Table 3. Distribution of incorrect (biased) responses Training Incorrect Answers # % # % 0 (No Warning) 103 49% 78 76% 1 (Warning) 108 51% 68 63% Total 211 146 69% Among 211 participants, 103, which account for 49%, received no training and 108, which accounts for 51%, received training. Among 103, who received no raining, 78 participants chose the wrong answer. This is equal to 76%. This percentage drops to 63% for those who receive training. The overall percentage of people choosing the incorrect decision is 69% In order to analyze the data further, logistic regression has been used. The dependents variable is whether the respondent answer is correct or false. So, the dependent variable is binary variable. The value of the binary variable is 1 if the answer is wrong and therefore there is cognitive bias. If the answer is right, the variable value would be 0 and there is no cognitive bias. There are two sets of independent variables. The first one is binary variable measuring the existence of training. If the respondent is given a training, the variable value will be 1. If there is no training, the variable value will be 0. The second set of independent variables is the 9 questions measuring the delayed gratification of the respondents. These variables are continuous variables measured using Likert- Scale with 7 points. Moreover, there will be interaction variables formed by multiplying the 9 questions of delayed gratification with training. The results of the logistic regression using SPSS is shown in table 4. Interactions are denoted by underscore. For example, Training_by_Q1 is the interaction of Training and Q1-417-

Table 4. Logistic regression analysis predicting an incorrect response B S.E. Wald df Sig. Exp(B) Q1.262.124 4.496 1.034 1.300 Q2 -.542.211 6.564 1.010.582 Q3.167.222.567 1.451 1.182 Q4 -.124.190.426 1.514.883 Q5.231.183 1.589 1.208 1.259 Q6.232.182 1.616 1.204 1.261 Q7 -.233.194 1.445 1.229.792 Q8.178.180.986 1.321 1.195 Q9.092.150.370 1.543 1.096 Training 2.098 1.148 3.340 1.068 8.148 Training_by_Q1 -.364.162 5.030 1.025.695 Training_by_Q2.635.242 6.877 1.009 1.887 Training_by_Q3 -.289.255 1.280 1.258.749 Training_by_Q4.190.233.665 1.415 1.210 Training_by_Q5 -.403.222 3.285 1.070.668 Training_by_Q6 -.357.212 2.823 1.093.700 Training_by_Q7.380.234 2.644 1.104 1.462 Training_by_Q8 -.303.219 1.915 1.166.739 Training_by_Q9.040.176.050 1.822 1.040 Constant -.348.731.226 1.635.706 Significant factors are shaded using gray color. The p value for such factors is below 0.10. for example, training is a significant factor because its P value is 0.068. Q1 is a significant factor with p value of 0.034. Moreover, the interaction term of Q1 and training is significant with p value of 0.025. Similarly, Q2 and the interaction term of Q and training are significant as their p Value is less than 0.10. Finally, the interaction terms of Training_by_Q5 and Training_by_Q6 are significant although Q5 and Q6 are insignificant. This study shows that the majority of students will commit the Insensitivity to Prior Probability bias. Literature states that cognitive biases are embedded in human mind and cannot be removed completely. This statement is supported in this research. Although providing training about the nature of such cognitive bias has improved the accuracy of answering the question correctly, still some people make wrong decision. So, by providing people with simple training about cognitive biases, it is expected that the rate of making biased decisions would decrease. Moreover, this research shows that delayed gratification can partially be used to predict the occurrence of Insensitivity to Prior Probability bias. LIMITATIONS AND CONCLUSION This study has two major limitations. The first one is that the sample consists primarily of students in a business college. Although these students eventually would graduate and work in business, we should be cautious not to generalize based on this sample. The second limitation is that the -418-

Insensitivity to Prior Probability bias is researched based on only one scenario. This scenario shows that people will ignore the prior probability of outcomes and would choose the wrong answer. However, in order to make conclusions of such cognitive bias, more than one scenario with different OM contexts should be developed to make sure that this cognitive bias is so common. Based on the authors knowledge, this the first study of cognitive biases applied to an OM context. Researchers in OM field can extend this study by developing scenarios in the Insensitivity to Prior Probability bias to verify the results shown in this study. Moreover, they can develop scenarios that tackles the other 12 cognitive biases proposed by Tversky and Kahneman (1974). REFRENCES Bendoly, E., Croson, R., Goncalves, P., & Schultz, K. (2010). Bodies of knowledge for research in behavioral operations. Production and Operations Management, 19(4), 434-452. Boudreau, J., Hopp, W., McClain, J. O., & Thomas, L. J. (2003). On the interface between operations and human resources management. Manufacturing & Service Operations Management, 5(3), 179-202. Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic perspectives, 25-42. Gino, F., & Pisano, G. (2008). Toward a theory of behavioral operations. Manufacturing & Service Operations Management, 10(4), 676-691. Kaufmann, L., Michel, A., & Carter, C. R. (2009). Debiasing strategies in supply management decision making. Journal of Business Logistics, 30(1), 85-106. Loch, C. H., & Wu, Y. (2007). Behavioral operations management (Vol. 2). Now Publishers Inc. Tversky, A., & Kahneman, D. (1974). Judgment under uncertainty: Heuristics and biases. science, 185(4157), 1124-1131. -419-