Decision support system is the area of information system, which is actually devoted towards supporting and improving human decision-making system. Cognitive decision bias can be defined as the deviation from the rational decision making (Arnott, Lizama and Song 2017). Rational choice is the one, which is actually based on decision maker’s current assets and possible outcome of the choice. Cognition in the decision-making process drops the quality of the decision support system. David Arnott described total 37 cognitive biases, which have major effect on the decision quality made by the human beings (Arnott 1998). This study will discuss the individual biases or bias taxonomies defined by David Arnott. Out of 37 biases developed by David Arnott, this study will describe 10 cognitive biases, which affect the decision quality of the managers in an organization. The study will also set clear evidences for each of the biases.
Memory bias or Hindsight Bias is also known as creeping determinism or knew-it-all along effect. Such bias defines the inclination of the people after an even has occurred. Such kind of bias is more likely to occur in organizational decision making, when managers are highly inclined to predict an even based on previous event. According to Cristea, Kok and Cuijpers (2015), memory bias is likely to occur in organizational decision making, when the managers are more likely to predict an event, which has little or no basis for predicting it. Furthermore, hindsight bias is also the psychological phenomenon, where the managers are inclined to judge the past disproportionately and more positively than judge the present scenario or event. Such multifaceted phenomenon can have huge impact on various stages of processes, designs, situations and contexts. On the other hand, Croskerry (2013) opined that hindsight bias leads to memory distortion, where the reconstruction and recollection of content can cause false theoretical outcomes in organizational decision-making process.
As per Liedtka (2015), memory bias or hindsight bias can often lead to extreme methodological problems in analyzing, understanding and interpreting the results of experimental studies. Moreover, the tendency of the managers towards predicting an event can overestimate the organizational decision making. In this way, such overestimation and prediction often lead to biased managerial decision in any organization. Furthermore, hindsight bias leads to inconsistency and disconfirmation in the present organizational decisions, when the managers are more likely to judge the present event based on the previous event. Therefore, the managers should always remove unnecessary details of the previous data and reconstruct the details in simpler way as needed in the present scenario.
While considering example of hindsight or memory bias, it can be said that the Managers of Cotton On have shown such biases in case of market entry to Hong Kong. While entering to Hong king, the managers predicted the sales volume in this new market based on the estimation of the market entry in New Zealand. Moreover, they used same sale techniques and product specification like New Zealand in Hong Kong Market. However, the factors affecting the sales figures of Cotton On in Hong Kong market are completely different from those of New Zealand market (Joormann, Waugh and Gotlib 2015). Therefore, the managerial decision seems to be overestimated and biased while entering in Hong Kong market.
Over confidence bias defines a well-established bias in which the subjective confidence of the manager on his/her judgment is quite greater than objective accuracy of those judgments. Such situation leads to biased organizational decision. According to Rapee et al. (2013), managers are more likely give priority to their subjective confidence of judgment over the objective accuracy of those judgments, when the confidence level among them is relatively high. Overconfidence is actually the instance of giving unnecessary priority to the subjective probabilities. Furthermore, it defines the situation, where overconfidence exceeds the accuracy level of a decision, which implies the people are surer about their judgment than they deserve to be in actually scenario. On the other hand, Black and Grisham (2016) opined that being overconfident the managers do not bother much about the current scenario and try to impose their presumed ideas on current situation without much evaluation. In such situation, the managers often misinterpret the current situation based on their overconfidence, which can even lead to biased organizational decision.
Making perfect forecast about the profitability, the managers of an organization should accurately consider and assess the uncertain factors for minimizing their effects on the business. However, overconfidence of the managers makes them reluctant to such assessment of uncertain factors and leads them towards taking biased organizational decision. Furthermore, overconfidence of the managers often hinders their ability towards looking for any innovative business opportunities (Johnson et al. 2013). Moreover, over precision of their decision sometimes lead to harmful effect on the success level of an organization. The act of placing their decision over above everything even in uncertain situation often leads to organizational crisis. Therefore, overconfidence of the managers often leads biased organizational decision, which in turn leads to failure of business success.
While considering the example of overconfidence bias in an organizational decision, it can be said that the overconfidence effect of the managers in Motorola has hamper its success level. Moreover, in the changing technological era of mobile platform, the managers of Motorola were overconfident over their backdated technological platform. They were rigid on shifting on 3G platform and adopt any other technically advanced mobile features. The managers were unnecessary imposing their overconfidence and were stick on their traditional managerial decision (Theregister.co.uk 2012). Such overconfidence of the managers ultimately led to business failure of Motorola and takeover of the organization by another company.
According to the viewpoint of Blumenthal-Barby and Krieger (2015), managers are often found to take decision based on the historical outcomes. Managers try to implement new strategies by correlating two diverse events in such a way that outcome will only be achieved when both work together. This correlation biasness is associated with the probability of two-event occurring at the same time and cannot be underestimated since significant outcome was assessed earlier. Koch, D’Mello and Sackett (2015) also pointed out the managers become extremely reliant on the correlated outcome and hence any alteration or new contradictory information is nullified at the root level. However, such biased approach for decision making often leads to fatal outcome. With globalization and internationalization, organizations have become extremely competitive and therefore historical and past records might not stand to be perfect for future decision making.
Often it is found that with the incorporation of technological advancement within the operational level of an organization, tends to reduce workforce requirement. Therefore, managers correlate their decision by taking two strategies such as organizational level technological advancement and human resource laceration. However, such decision impacts in talent management as with due course of time, the concerned managers waste more budget in recruitment and training of new hired candidates, which would perhaps be less if internal succession planning could have been followed. Therefore, such correlated decision can be considered as biased.
While considering the case of Volkswagen, it can be said that managers were the sole responsible for poor decision making that halted manufacturing and accelerated resource consumption. Volkswagen tried to imitate the Kanban system as of Toyota and the managers thought of making the system fully automated. This was a change management strategy. On the other hand, managers thought of reducing the number of line managers and supervisors from production line so that expenditure could have been limited (Icmrindia.org 2016). The managers were influenced by the outcome that Toyota received by using the Kanban system and therefore thought of reducing the cost of human resource. However, later it was found that the cost of machine handling and supportive activities was taking more cost than making the system partially automated. Eventually later there was the requirement of experienced line managers for maintaining and operating the recycle system but the managers failed to recruit those required talent. Therefore, it can be said that coupled and correlated decision does not always provide similar outcome, but rather such managerial decision is considered as biased.
Every operational manager tries to fulfill the corporate objectives by strengthening policies, strategies and decisions. In desire bias, the managers are more likely to overestimate the probability of outcome than its warrant of occurrence. Such decision bias is more likely to occur in case, where managers are more likely to be confident over the probable outcome of a decision. Moreover, in this bias, the managers are more likely to follow the probability of the outcome and underestimate any risk in the decision. According to the viewpoint of Goschke (2014), quite often it is found that managers try to become more subjective while fulfilling their desire. With the sense of achievement and recognition, managers try to exemplify such planning, which perhaps return more brand value for the organization. Conceiving the thought and directed coagulated indication from other managers, desire oriented decisions are taken. Such kinds of decisions are more related to Control bias, where managers fail to identify the chance of failure.
Liedtka (2015) pointed out that experience and knowledge are both extremely essential for managers to create a concrete decision but often due to lack of experience managers fail to implement their knowledge. Desire oriented managers mostly rely on theoretical underpinning and possible outcome with taking least consideration of historical or past outcome. This indicates the lack of experience. Desire oriented managerial decisions are mostly taken in those companies that rely on continuous innovation. However, Evans and Stanovich (2013) pointed out that managers mostly fail because subjective probabilities are set significantly higher than objective probabilities. It can be said that few elements of desire oriented overconfidence decisions are functional but such biased desire from managerial perspective often leads to poor quality of decision.
Biased managerial decision can lead to perish an organization, and similar outcome was prominent for Nokia. In the year 2010, Olli-Pekka Kallasvuo was the CEO of Nokia and by that time, Nokia introduced two flagships N97 and N8, which would run only on Symbian platform. The main desire of the CEO was to make Nokia Symbian as the standalone Operating System that would compete with both iOS and Android. However, such decision was completely desire based and due to overconfidence. The CEO had very less knowledge of smartphone market evolution and possible networking frameworks available in the market that would serve as the application development partners. Therefore, Nokia even though was the top competitor with quality as the leadership benchmark till 2009, failed to satisfy new demands of consumers (Harvard Business Review 2011). Olli-Pekka Kallasvuo had a good desire to create a healthy market competition but due to lack of experience and consequence such desire oriented strategy led to perish the organization. Therefore, it can be said that wishful thinking can be only influential if the implementation does adhere to realistic outcome. Managers must not be biased while making corporate level decision but must think for sustainable approach for best outcome.
In cognitive psychology and decision-making process, Conservatism bias is the bias in the human information processing, which defines the tendency towards revising one’s belief insufficiently, when present with new data sample or evidence. According to Phelps, Lempert and Sokol-Hessner (2014), conservatism bias refers to the revision of human belief, where people are more likely to over-weigh their prior distribution or base rate and under-weigh new sample of evidence. Persons need to update their traditional belief, when they are presented with new set of information becomes available. As per the concept of this bias, the estimates are not often revised appropriately even on receipt of new significant evidence. Conservatism bias tries to protect the waste and advantages of prior cognitive effort towards making any decision. However, such conservative ideas often seem to create biased organizational decision in changing business environment. On the other hand, Montibeller and Winterfeldt (2015) opined that conservatism bias also seems to be irrational as per the future prospects aspect of rationality. Opinions are changed in an orderly manner, but people change their opinions very slowly than they should change. In case of organizational decision making process, the managers having effected by conservatism effect often lead to biased decision in dynamic changing business environment.
Conservatism biases are more likely occur in organizational decision, when the managers are resistant to change their traditional ideas. As per Guitart-Masip et al. (2014), cognitive bias limits the innovative thought process of the managers, which often lead the organizations towards lagging behind their other competitors changing market demand and situation. Moreover, such bias actually restricts the innovative organizational decision based on the new information and evidences in the dynamic market situation. Therefore, in most of the case, cognitive bias loses the innovative and unique business opportunities through under-weighing the new evidence in the market.
While considering the example of conservative bias, the evidence of Nokia can be heighted. The managers of Nokia over-weighted their traditional ideas and under-weighted the new evidence of market information. Moreover, the managerial decision of Nokia failed to meet the changing customer demand in regards to mobile set. Moreover, the managers of the organization ignored the information about customer demand and competitors. Moreover, the competitor organizations were dominating the markets with their android operating system and there are also high demands among the customers for this android operating system (Harvard Business Review 2011). However, the managers simply under-weighted this information and continued with their traditional operating system. This cognitive bias led Nokia in lagging behind the competitors and ultimately led to failure of the business and takeover by Microsoft.
Complexity bias is dependent on the belief that complex situations are quite better than simple ones. However, such bias also denotes irrational preferences for the complex situation over the simple situation, which is quite faster, cheaper and safer. According to Stiegler and Tung (2014), time pressure, negative environmental factors and information overload enhance the perceived complexity of task. In such situation, the high-level complexity of the situation often leads to wrong organizational decision made by the organizational managers. Most common source of task stress is extreme time pressure within the organizations. Moreover, too much pressure placed on making a decision actually drop the quality of the organizational decision. On the other hand, Saposnik et al. (2016) opined that information overload and other environmental factors also enhance the perceived complexity a task. Such factors actually negatively impact on the decision quality of the managers of an organization. Complexity bias limits the capability of the managers in taking suitable and right decision as per the situation. The level of perceived complexity hinders the capability of the organizational managers towards drawing quality decision for their organizational success.
The complexity biases block the innovative and planned thought process of the managers, which ultimately hamper their decision quality towards bringing organizational success. Furthermore, high level of perceived complexity makes the organizational managers to much pessimistic, which hinders their capability towards properly handling the complex situation (Helfat and Peteraf 2015). Therefore, they become more prone to take ineffective and biased decision, which often conflict with the actual situation. Therefore, it can be said that too much pressure enhances the perceived complexity level of a situation, which can drop the quality of decision taken by organizational managers.
While considering the example of complexity bias, the instance of 7/11 can be considered. The managers of this organization were highly under the pressure for increasing the profit level of Australia franchisee. The managers of the organization have perceived this task to be very complex due to time pressure and information overload for the business operation. In such situation over complexity of the situation towards profit enhancing has created huge pressure on the organizational managers. Such complexity level led the managers towards taking immediate decision, which has dropped the quality of the decision (Ferguson and Danckert 2016). Moreover, they took the decision of enhancing the working time of the employees by reducing their salary package. Such unethical and biased decision actually led to legal complication for the organization.
Attenuation Bias defines that decision making can be highly simplified through significantly ignoring or discounting the level of uncertainty. The crudest way of coping up with the uncertain decision behavior is to simply consider the uncertain factors as certain. However, avoidance of uncertain factors actually hampers the quality of the decision making. According to Shadlen and Kiani (2013), attenuation bias simply occurs, when managers are more prone towards avoiding uncertain factors in the decision-making process. Moreover, avoidance of uncertain factors in the decision-making process simply underestimate the impact of such factors on business process. Therefore, avoidance of such uncertain factors ultimately affects the business negatively. On the other hand, Mishra (2014) opined that attenuation bias miss out the uncertain factors effecting organizational success. Therefore, having such bias in decision making, the managers of an organization do not also plan for the strategies towards dealing with the uncertain factors of business. Therefore, later on, the managers become failed in handling those business uncertainties and minimizing their effect on business process.
In often case, managers having Attenuation bias find that the market for their business has been changed dramatically with the exploitation of new technology. It often hampers the overall business success of their organizations. In most of the cases, the managers are aware of such changes and uncertainties in the market and still remain indifferent about those uncertainties (Santos and Rosati 2015). Later on, such uncertainties cause big hindrance for the organizations towards getting highly level of success. In this way, attenuation bias actually reduces the quality of managerial decision, where they completely avoid the risk of uncertainties on business.
While considering the example of Attenuation bias, the evidence of Microsoft’s managerial decision can be highlighted. Such instance is more evident in case Microsoft Lumia Smartphone brand. In case of this brand, the organization has not assessed the changing customer preferences of the customers. Moreover, the managers have not considered the changing customers preferences in the business decision, while launching Microsoft Lumia Smartphone. Ultimately such changing consumer preferences have become uncertainty for the organization. Moreover, the managers did not assess the changing preferences of the customers in terms of user interface, hardware and apps and continued with traditional Smartphone features through applying simplified business decision (Moore 2016). In this way, the ineffective managerial decision has caused big failure for Microsoft Lumia with the changing consumer preferences for advanced technology.
In imganibality bias, an event can be more probable, if it can be imagined very easily. Imaginability is the ability of human being towards constructing an abstract model in the memory regarding an event. People are more often relied on imaginability, as it facilitates in assessing the probabilities and predicting the value of an outcome. According to Summerfield and De Lange (2014), imaginabiity allow the managers towards taking business quickly and instinctually. However, the heuristics based on imagine are often inaccurate. People often fail to make proper judgment of this imagined data due to lack of true evidence. Moreover, managers are more often make their judgment and decision based on the imagined data and not on true evidence. Therefore, in most of the case, the outcome of the decision fails in reality. Moreover, in such imagination, the managers often ignore the risks associated with an event in real situation. In this way, decisions made on imaginability bias are often inaccurate and ineffective, as it ignores the risk associated with the event. Therefore, imaginability bias is more likely to reduce the quality of the decision taken by the managers of an organization. On the other hand, Marshall et al. (2013) opined that imaginability bias always encourages the managers towards taking decision based on abstract model. The factors affecting the decision making of the managers may not be based on true statistics or evidences. Therefore, the decisions may not be valid enough towards fulfilling the actual objectives of an organization.
People always feel comfortable in making intuitive prediction, which can even be based on insufficient information. There is ineffectiveness in the search set towards making any rational decision, which can ultimately reduce the rationality of the decision. Therefore, it can be said that imaginability bias lead to biased decision making, which can hamper the organizational effectiveness.
While considering the example of imaginability bias, the example of Coca Cola can be taken as evidence. Moreover, the evidence is clearer in regards to the Cole Life brand of the organization. In case of this brand, the managers of Coca Cola took decision based on imaginability. They launched this product as a drink, which is perfect for health-conscious people. They have imagined that green packaging of the drink will be highly popular to the customers. However, the packaging has no relevance with the quality and ingredients of the product. The decision was completely taken under imagination without much incorporation of real fact (NewsComAu 2015). Therefore, Coke Life ultimately faced huge failure in the market.
The more voluminous and redundant the data the more confidence may be expressed in the accuracy and importance of the decisions. Redundancy bias occurs, when people overestimate the probability of the occurrence or the data set, which are repeatedly presented to them. Voluminous data set can ensure the accuracy of the decision. However, people should always assess the validity of the voluminous data. According to Schwager and Rothermund (2013), redundancy bias is highly prone to occur, when the managers of an organization consider same and repeated message or data from various sources, but all the sources are actually false and erroneous. In such situation, the erroneous data used in the decision-making process ultimately leads the managers towards making low quality decisions. On the other hand, Capestany and Harris (2014) opined that redundancy bias is also likely to occur, when the quantity of the data present to the decision maker is quite large. In case of organizational context, excessively voluminous data presented to the managers may confuse them with excessiveness of the data volume. Therefore, such excessive data lead to information overload for the managers, which in turn reduce the quality of the decision.
Redundancy bias is also likely to occur, when managers do not evaluate and assess much the validity and accuracy level of the voluminous data presented to them during decision making process. Therefore, redundancy of the data makes the managers incapable of identifying the accuracy level within the data. In such situation, consideration of inaccurate data in the decision making process leads the managers in taking low quality and biased decision for the organizations (Newell and Shanks 2014). Furthermore, bias in the decision making can also be occurred, when the data presented to the managers is perceived to be larger.
While considering the example of redundancy bias, the evidence of the managers of PepsiCo can be highlighted. Moreover, the example can specifically be highlighted for launching the brand Mountain Dew Sport. The decision of the managers for launching Mountain Dew Sport was actually biased with redundancy in the data presented to them. Moreover, the drink was actually manufactured with only 2 calories for the benefits of the athletes. However, with the redundancy of the data presented to the managers, they have wrongly evaluated the calorie level of the drinks produced by various competitors in the market. In the huge volume of data, they missed out the information that some competitors were using no calories in their sport drinks (Frey, Schulz-Hardt and Stahlberg 2013). In this way, the redundancy of data led to low quality decision making for Mountain Dew Sport drink, which even caused its failure in the market.
Similarity bias is more likely to occur, when people tend to make judgment based on similarity. People are more likely to make judgment based on the similarity between the current situations and the prototypes of those current situations. Moreover, in similarity bias, an event is mostly judged by the degree of similarity with the class to the event is perceived to belong to. According to Swann et al. (2014), similarity bias occurs, when the managers of an organization select the past event similar to their event as opposed to other events, which appear to be quite different. In such situation, the inaccuracy of the previous similar event can have huge influence on the current decision or event. Therefore, managers having similarity bias are prone to make biased decision in their organizational context, when the decision is influenced by the inaccuracy in past similar decisions. On the other hand, Meissner and Wulf (2013) stated that similarity bias can also be enhanced by other biases of the biases, where they unconsciously make their mind on someone in the initial stage. Furthermore, with such unconscious mind, the managers can even incorporate biased information of the similar other event. In this way, the biased information of the similar other event can cause biases in the current event. Therefore, the similarity bias actually drops the decision quality of the managers.
While considering the example of similarity bias, the evidence of Nintendo Co., Ltd of Japan can be highlighted. Moreover, the evidence can be more specifically highlighted for their decision about Nintendo Entertainment System (NES) in United State. It is actually a video game device. The organization has actually made such video game in the middle of video game depression. Initial of this video game was showing that Nintendo Entertainment System was not made poorly and it was quite similar to interactive television gaming (Li et al. 2013). Moreover, the customers perceived the video game quite similar to interactive television gaming. In this way, the video game had not got much popularity in the market. Therefore, it can be said that the managers of the organization were posed by similarity bias, which led to the failure for the product.
Conclusion
While concluding the study, it can be said that cognitive bias defines the systematic pattern of deviation from the rational in judgment, whereby the inference about the situation can be drawn in illogical fashion. Hindsight bias is more likely to occur, when managers are more likely to predict an event based on previous occurrence, but in real situation, it has little or no basis for prediction. In overconfidence bias, the managers are more likely to be confident over the subjective confidence rather than the objective accuracy of the judgment. In this way, overconfidence of the managers actually leads to bias in the decision making. In desire bias, the managers are often relied on the probability of an outcome rather than warrant of the occurrence. In this way, the probability of the outcome often associates risk in the actual event, which ultimately cause biased decision making6. The managers having conservatism bias are more likely to believe on the traditional fact even on presentation of new set of evidence. In this way, the old data often create biased decision in the dynamic changing business environment. In case of similarity bias, the managers are more likely to take decisions based on the similar other event. Therefore, the inaccuracy of the information in the similar other event actually hampers the current decision.
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