Managerial Economics/Managerial decision making

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How should managers make decisions?

Marginal decision making is an effective organisation and coordination of internal management for the realisation of strategic decision making, thus enabling enterprises to carry out production technology and economic activities normally. These decisions are applied through a framework of core management principles. Application of the appropriate core managerial practices are essential for success in this managerial economic principle.

Beliefs and Subjective Probability[edit | edit source]

In practice, we don't know the true probability of an event occurring, but we rely on beliefs to help us form estimations of probabilities and payoffs. Beliefs can be formed from the availability of information, or prior knowledge on a situation. Beliefs give rise to subjective probability.

Subjective probability is our best estimate of how likely an outcome is to happen. It is derived from an individual's personal judgment or previous experiences, based on all the available information. There are no formal calculations involved in determining the outcome and it is often based on non scientific evidence, e.g. "gut instinct" when making a trade. Note that probability always takes a value between 0 and 1.

Expected Utility (EU)[edit | edit source]

Expected Utility is the utility that an individual or an entity is expected to gain under a number of circumstances. It is calculated by taking the weighted average of all possible outcomes, where the weights correspond to the probability of a particular event [1]. When considered mathematically, the expected utility function can give us an indication of whether an individual is risk seeking, risk averse or risk neutral. The EU function can be described as the sum of the product of probability and utility for each possible outcome. If x1, x2 and x3 represent the utility for three possible outcomes of X, while y1 , y2, y3 are the probabilities of the outcomes, EU can be represented in the following general sense;

Assumptions of Expected Utility Theory[edit | edit source]

1) Linearity in probabilities. In other words, the utility value of each choice is multiplied by an equal probability-weighted average of each choice, and the values are summed up to achieve the total expected utility value.

An example would be the chance of winning 1 out of 4 prizes, the four prizes are $1,000, $10,000, $100,000 and $500,000. Therefore, the EU of the prizes is represented as;



The above example shows that each probability is weighted at 25% for all choices, as the probability is equally weighted.

But in reality, people have different probability-weighting functions, where large probabilities are likely to be underweighted while small probabilities are overweighted. For example, people might get a perceived probability of 30% after putting a 20% in their individual probability-weighting functions, while getting 60% after putting 80% in their functions.


2) Preferences are over wealth (asset integration) rather than gains and losses

An example: A has $1,000,000 and loses $100,000 so A has got $900,000 left; B has $1,000 and wins $100 so B has got $1,100 now.

If preferences are over wealth, then certainly A is happier than B as A has more wealth overall; but if preferences are over gains and losses, B will be happier because B has gained some extra money while A has lost some money.

Risk Seeking[edit | edit source]

If an individual is risk seeking, they are not indifferent between a guaranteed sum of $50 and a coin flip that determines whether they get $100 or $0, even though they have the same expected value. The risk seeking individual derives more utility from the risk. The expected utility function is convex if represented on a graph. With utility units on the y-axis and wealth($) on the x-axis, a convex shaped graph describes the increasing marginal utility of being risk seeking. Furthermore, from the title of "risk seeking", it is more likely for an individual to take on riskier gambles. Risk seekers understand the value of their risks in comparison to their rewards, and, in contrast to the risk averse, value the chance of attaining these rewards, which tend to be relatively high, more than the risks involved. An example would be when an individual with no knowledge of football takes a gamble on a match between France and England, with the payout for France winning at $20 and England winning at $100. Although the likelihood of England winning the match is very unlikely, and the most reasonable option would be to choose France, due to the payoff, risk seeking individuals would be more likely to take a gamble on the lower outcome, higher reward decisions. Therefore, the risk seeking individual would choose to gamble and take action by betting on England winning. In regards to risk seekers in the investing world, examples would be investing in emerging markets, developing countries currencies, commodity funds, etc. These investments usually show both high risk, high rewards.

Risk Averse[edit | edit source]

Conversely, risk averse people would prefer a guaranteed $50 then a 50/50 chance at getting $100, even though both have the same EU (expected utility). The utility they derive from a certain $50 is more than a bet with that expected value; which might only be equal to a guaranteed $40. This difference is called the risk premium. For the risk averse individual, the expected utility function is concave if represented on a graph. A concave shaped graph describes the diminishing marginal utility of being risk averse. Without more background information, a risk averse individual would not take on risky gambles. Following the previous example on football betting, being risk averse, an individual will not place any bets without prior knowledge of football. Alternatively, if the individual did have prior knowledge of the game, or is used to basing their chances off of stats, then a risk averse individual will almost always bet on the team with lower returns with known risks (or in this case, likely outcomes), rather than higher returns with unknown risks (unlikely outcomes). Therefore, if betting on football outcomes, the individual would bet on France (current champions) over a weaker side. In regards to investors, when those who are risk averse decide, between different investments with the same return, and with different levels of risks, they will always prefer the alternative with the least interest. Therefore, risk averse investors would likely stay away from the risker invesments such as stocks and commodities, and, rather, focus on safer options such as tresury bills, life insurance and governement bonds.

Risk Neutral[edit | edit source]

If an individual is risk neutral, they are indifferent between a guaranteed sum of $50, or a coin flip that decides whether they get $0 or $100, because both have the same expected utility ($50). The expected utility function is a straight line if represented on a graph, which describes the constant marginal utility achieve for each unit of wealth obtained. Individuals who are insensitive to risk effectively ignore risk completely when making decisions. A risk neutral person would only look at the gains while ignoring the losses, thus ignoring the risk. Risk neutral differs from risk seekers as risk seekers are drawn to risks, whereas, risk neutral individuals are impartial and therefore can't be more likely to undergo an action based on that criteria, they must be completely unaffected by the size of the risk, rather, they focus on the best possible return. In the Football example, the risk neutral person would bet on England as it has the highest payout regardless that it is the riskiest option. In terms of investment, there are no obvious areas for risk neutral investors as their investments depend solely on the rewards gained.

Risk Aversion[edit | edit source]

Risk aversion refers to the behavioural approach that consumers, investors and managers may adopt in regard to risk. Hence, when faced with a risk based decision, all attempts are made to minimise the uncertainty of that decision and its outcomes.

  • For example, if someone was risk-averse they would forgo a 50% chance to win $100 and 50% chace to lose $200 as opposed to a 50% chance to win $100 and a 50% chance to lose $150

Alternatively, a risk-averse person would give up the gamble of a 50% chance to win $100 and 50% chance to lose $100 rather than a 50% chance to win $200 and 50% chance to win $100.

It can be implied that an individual is risk-averse if he/she has a utility function which exhibits diminishing returns.

The Risk Premium[edit | edit source]

Risk Premium[edit | edit source]

The maximum amount that a risk-adverse person would pay to avoid taking a risk. The risk payment is an excess that an individual will pay on top of the initial price such that the risk of losing is reduced significantly. An example could be when a risk averse individual is given two options, firstly, to place a $50 bet in the casino and risk losing; or secondly, pay an exit fee of $10 and leave the casino at $40. By choosing the latter, the premium that the individual paid was $10 to avoid taking the risk of losing $50.

Equivalently, the risk premium is the minimum extra compensation (premium) that a decision-maker would require to willingly incur a risk. An example would be if a small company wanting to entice investors into investing in their company, they could sell corporate bonds with higher yields or interest to compensate investors for the risk that they are taking on.

Risk premium may arise for a few reasons. The firm who borrowed the money, may default on its contractual repayment obligations (default risk premium). The investor may have little seniority in presenting claims against against a borrower who is bankrupt (seniority risk premium). Investor may not be able to sell his/her security interests (liquidity risk premium). Debt repayment occurs earlier than expected (maturity risk premium). Lastly, returns that the investor receives may be very volatile, exceeding expectations in one period and crashing below expectations in the next period.[2]

The Equity Premium[edit | edit source]

The expected return on the aggregate stock market less the riskless investment return (Treasury bill) rate. It is derived from equity minus bond returns. The premium is supposed to reflect the relative risk of stocks compared to "risk-free" government bonds.

Equity Premium Puzzle[edit | edit source]

The Equity Premium Puzzle (EPP) is the formal description first introduced by Rajnish Mehra and Edward C. Prescott in 1985 regarding an observed anomaly in the equity premium in the US market. In a paper titled The Equity Premium: A Puzzle, Mehra and Prescott outlined the basis of the EPP as the observed divergence in the expected equity premium based on a reasonable assumed level of risk aversion in the market, and the average equity premium actually observed from the historical data.

Mehra and Prescott’s paper analysed data from 1889-1978 and highlighted that “the average real annual yield on the Standard and Poor 500 Index was [7%, while the average yield on short-term debt was less than [1%]”  finding an excessive equity premium of 6%. However, these equity returns are higher than what it should be, in turn rewarding investors holding stocks instead of government bonds assumed to be risk-free. Essentially the EPP is describing the problem between the difference between the expected return on aggregate stock and the return on risk-free investments are too large and does not reflect the compensation of holding an aggregate of stocks. Thus, overcompensating investors holding aggregating stocks.

Solutions to the puzzle have consequently attempted to either address the apparent misunderstanding of consumer preferences and risk aversion or question the statistic validity of the existence of the puzzle.

Proposed Explanations:[edit | edit source]

The prospect theory with additional considerations in taxes, liquidity and debts attempts to explain the EPP. However, the main issue of the EPP where investors are still over compensated for holding risky assets is not completely explained.

Myopic Loss Aversion and the Equity Premium Puzzle:[edit | edit source]

In short the Equity Premium puzzle refers to the extremely positive performance of stocks when compared to bonds over the last century. Due to the volatility of the stock market, if you are a shareholder in a company and you use a shorter evaluation period (once per quarter) while being a loss averse individual you will oftentimes have a negative shareholders' value. Whereas, if you are still the same loss averse person and you evaluate your shareholders' value less frequently (once per annum), you are far more likely to receive a positive shareholders' value. Generally speaking, even individuals with long-term investment goals check their portfolios to frequently and due to sensitivity to losses make poor decisions for the long term. This is how myopic loss aversion can explain the equity premium puzzle.

The Total Risk Premium Puzzle[edit | edit source]

The Total Risk Premium Puzzle (TRPP) addresses the identical problem in the Equity Premium Puzzle (EPP); inordinately high risk aversion factors when compared to those predicted by dominant models. However, the TRPP goes further, addressing the total wealth portfolio including investments such as housing, which demonstrates an even more pronounced problem. In a paper submitted to the National Bureau of Economic Research titled The Total Risk Premium Puzzle (2019), Oscar Jorda and co-authors outlined the existence of this puzzle as an extension and considerably more problematic puzzle of the EPP. The inclusion of a second asset from a different class, in this case, housing, allowed the authors to comment on the risk premium anomaly in a total wealth context. In this case, the data demonstrated an even greater divergence of the assumed premium based on modeled risk aversion and the actual observed premium. In fact, the sample data suggested that the risk-aversion parameters operating in investment decisions when considering housing asset investment and total wealth investment are higher than those operating in equity investment decisions, “often by a factor of 2 or more” (The Total Risk Premium Puzzle, 2019). This puzzle deepens when considering that the sample data used in the paper suggested that housing was both a stronger and safer investment, with a lower standard deviation on higher returns in the “World Full Sample”.

Allais Paradox -Prospect Theory[edit | edit source]

  • The Allais paradox is a paradox of decision theory proposed by French economist Maurice Allais in 1952. Allais designed this paradox to prove that the expected utility theory and the rational choice axiom based on the expected utility theory have logical inconsistency.
  • Allais noted specifically that people perceive a smaller difference between 1% and 2% than between 0% and 1%. Consumers value complete certainty an inordinate amount.

Essentially, the paradox explains that a risk adverse person who chooses an option of 0% of losing in one gamble might prefer a riskier gamble if the choices presented to the risk adverse person both carrying similar high risk. Logically, the risk adverse person should supposedly choose the less risky option even if both choices were risky, however, the paradox happens when the risk adverse person chooses the option with a slightly higher risk.

To further explain the allais paradox, an example would be if a person is faced with a gamble between two options. Option A has a 100% probability of winning $10 and 0% of losing and option B has a probability of 80% to win $12 and a 20% probability of winning $0. A risk adverse will pick option A where the probability of winning $10 is 100%.

An additional scenario such that another opportunity to gamble arises, in this gamble, Option A has a 30% probability of winning $10 and a 70% probability of winning $0 and option B has a 25% probability of winning $12 and a 75% probability of winning $0. Logically, a risk adverse person as explained by the expected utility theory should choose what he/she chose in the previous gamble which is Option A. However, in the Allais paradox, the risk adverse person chooses Option B, which is slightly riskier. These paradox describes the discrepancies of the expected utility theory.

Prospect Theory[edit | edit source]

Prospect Theory was put forward by Kahneman and Tversky in 1979 to describe how a person evaluates a risky prospect in relation to some status quo position. It describes how people think in terms of expected utility relative to a reference point rather than absolute outcomes. Prospect theory is the basis for hypothesising that the satisfaction from avoiding losses exceeds the anticipation of equal-value prospective gains. It implies that sellers should distribute trial products, take prospective income in payment and full-line force.[2]

Cumulative Prospect Theory[edit | edit source]

In 1992, Kahneman and Tversky published an updated version of Prospect Theory, namely "cumulative Prospect theory".[3] It aims to improve the limitation of the predecessor, solving the problem where gamble predictions could only apply on at most 2 nonzero outcomes. Since then it has been widely adopted in economic analysis. The updated theory revolves in 4 elements, "1) reference dependence, 2) loss aversion, 3) diminishing sensitivity, and 4) probability weighting. [4] Details of the modified theory could be found in the original paper "ADVANCES IN PROSPECT-THEORY - CUMULATIVE REPRESENTATION OF UNCERTAINTY".

Modification of expected utility to prospect theory:

- Linearity in probabilities, it should focus on the individual function rather than standard number in expected utility theory. In prospect theory, it assumes a misconception in the weighted probability of the expected utility. Probability is not linear nor just a number, it is a function.

- Preferences on overall utility (wealth) rather than gains and losses. e.g. Expected utility theory assumes people would feel better off if he/she has 1 million while losing $100, rather than having only 1 thousand while gaining $100. Prospect theory suggests people's happiness are often associated with changes in gain and losses based on a reference point rather than total utility.

Background[edit | edit source]

Kahneman and Tversky (1979) determined three main factors which influenced the creation of Prospect Theory by extending the considerations of the Theory of Expected Utility (Bernoulli, 1738).

Expected Utility has been detailed above however there are a few components to the model which are considered incorrect. Firstly, this model is reference independent and thus assumes that decision-makers value their outcomes by only the utility of their final position. In addition to this, Bernoulli’s model is concerned with the long run results rather than the short term effects of how a person’s satisfaction is affected. Finally, the Expected Utility theory does not consider the pain or joy associated with loss or gain respectively. Thus, the combination of long term consequences and an inability to measure emotional responses, Bernoulli’s model is unable to maximise the utility of an outcome at the moment a person experiences it.

Prospect Theory takes these shortcomings and establishes a theory that involves an emotional reaction and is effective in the short run. Kahneman (2003) first established that preference is reference dependent, as are intuitive evaluations of outcomes and therefore, a reference point is vital when it comes to choices. Kahneman (2003) coined the term “Bernoulli’s error”, which refers to the focus on the utility of the final position rather than a reference point. In their research, Kahneman and Tversky stated that “utility cannot be divorced from emotion, and emotions are triggered by changes”. While Bernoulli clearly separates the two, Prospect Theory reflects the emotions associated with the transition from one state of wealth to another. In addition to this, Kahneman determined that there is an abrupt switch from risk aversion to risk seeking which is not explained by a utility function for wealth and this led to the creation of the Prospect Theory Value Function (see below).

Probability misconception[edit | edit source]

When one option in a scenario is underweighted, the probability weighting tends to bias the other option. If on the other hand, the probability of both options are in the same range, no bias would be introduced by a ‘smooth’ probability weighting function. One should overestimate the low risk of flying, being robbed in a big city, and taking a bus in Jerusalem. The high risk of smoking and taking drugs should be underestimated.

There are two sets of separate gambling:

1. choosing option A with probability 100% gaining 3 dollars, or option B with probability 80% gaining 4 dollars and probability 20% having nothing.

2. choosing option C with probability 25% gaining 3 dollars (probability of 75% having nothing), or option D with probability 20% gaining 4 dollars (probability 80% having nothing).

Results show more people choose A and D, although logic consistency should be chosen A and C.

Logic: Firstly, more people choose option A showing risk aversion, as EV(A)=3*100%=3, less than EV(B)=4*80%+0*20%=3.2

Secondly, by assessing probability for gamble 1 between two options: U(3) option A > 0.8 * U(4)+ 0.2* U(0) option B, thus rational choice should be A. ; whereas probability for gamble 2 is: 0.25* U(3) option C > 0.2* U(4)+ 0.05* U(0) option D, suggesting option C should be logically right.

Implications: people tend to be risk aversion when gaining is more certain, and tend to take more risks when the potential probability of loss is greater enough.

Prospect Theory Value Function

Prospect Theory Value Function[edit | edit source]

The Prospect Theory Value Function shows the value of a gain or loss derived from a neutral reference point. The curve is 'S-shaped' and centred around the reference point. In the positive domain of gains, it can be observed that the curve is significantly less steep than in the losses, unlike the expected utility model. This reflects the loss of aversion exhibited by people. Additionally, the curve is concave in gains and convex in losses, representing risk aversion and risk seeking behaviour respectively.

Implications:

  1. Loss aversion: in situations where both gains and losses are possible, losses are given more weight;
  2. Reflection effect around the reference point:
    • When all outcomes are gains - risk aversion
    • When all outcomes are losses - risk seeking

Biases in Risky Decision Making[edit | edit source]

Myopic Loss Aversion

The combination of a greater sensitivity to losses than to gains and a tendency to evaluate outcomes frequently. The more frequently you check the portfolio, the greater you feel your loss in the short term which can lead to pulling your assets out and missing out on the potential long term gain.

  • In simpler terms, individuals participating in risky decisions don't like to lose, so they research for as much information as possible or look at their overall portfolio value as frequently as possible trying to avoid every loss in the short term, this can lead to decisions that are detrimental to long term profits.

Certainty Effect

The disproportionate psychological effect from a reduction in probability for the desired outcome when the reduction is from a place of certainty or uncertainty, IE. 100% to 80%, and 80% to 60%. People tend to overestimate an option with relatively low risk and underestimate an option with a relatively high risk.

Gambler’s Fallacy

The mistaken belief that given a past event or sequence of events, future events are more or less likely to occur. This often occurs in gambling, hence the name. If the roulette ball landed on black fives times in a row, believing the ball to be more likely to land on red next would represent a failure of understanding how a truly independent random process occurs. For example believing that the probability that a coin toss will come up tails is now higher because the last three tosses came up as heads, but statistically, it is 50/50 every time the coin is tossed.

Correlation Neglect

The failure to consider the correlation of events when evaluating outcomes, leading to a double-counting effect. In consequence, beliefs become overly sensitive to the ubiquitous "telling and re-telling" of stories and can exhibit huge swings.

Ambiguity Aversion

The statistically unfounded preference in decision making agents for known risks over unknown risks. It is also known as uncertainty aversion. An example would be when given a gamble, gamble A, involves placing 20 white balls and 20 black balls in a bag, and if the white ball is drawn, the participant wins $10. This information is known to the participant. A second gamble, gamble B, involves the same rules but the amount of white and black balls are unknown. Therefore, ambiguity aversion explains that the participant would choose the gamble with a known probability.

Availability Heuristic

The availability heuristic relates to the cognitive budget that affects decision making. Essentially, it is a shortcut where more recent information, and information with a greater impact, is relied upon more heavily as it is immediately available to the decision-maker. The heuristic can also operate on the notion that the recalled information is more important than alternate information that is not readily recalled. For example, recent media coverage of terrorist attacks may cause an agent to overestimate the risk of being involved in a terrorist attack.

Overconfidence

The phenomenon in which decision-making agents typically have a subjective view of their judgments as greater than they are in objective reality. Overconfidence can be defined in three distinct ways: overestimation of decision-makers actual performance, over placement of their performance relative to others and over precision in expressing the belief that the maker’s decision is right.

Confirmation Bias

The phenomenon in which decision-making agents interpret information that confirms conclusions they already held. These agents are selective on the information, such that they are biased towards finding information that supports their prior statement in which they feel strongly about. For example, when voting, decision-makers may notice or interpret information about vote candidates that confirms with conclusions they already had. Another example is if someone holds the belief that left handed people are more creative, they will search for evidence that supports this and therefore interpret a left-handed job candidate differently to how someone else might.

Authority Bias

A bias largely prevalent in hierarchically structured organisations, authority bias is observed when an individual places greater weight on an idea or opinion formed by someone in an authoritative position. This favouritism is independent of the quality or relevance of the idea/opinion but is caused by a predisposed tendency to agree with individuals in higher positions of power. This bias can become stronger or weaker depending on whether a business is organised in a centralised or decentralised decision making structure.

Anchoring Bias

Present when an individual uses a reference point to anchor their decision on. Normally, these reference points are not sufficiently accurate or are not well informed for an individual to mentally compute an optimal decision. The most famous example of this was conducted by Tversky and Kahneman [5] In this study, participants were asked to spin a wheel to select a number between 0 and 100. Following this, participants were asked how many African countries were included in the United Nations. Results showed that individuals who rolled a higher number gave higher estimates to the question than individuals who rolled lower numbers. This was conclusively a result of participants anchoring their guesses on an initial, completely arbitrary reference point. Anchoring bias is also one of the basic irrational behaviours that is used by marketing companies in order to increase consumers maximum willingness to pay for a product (see Managerial Economics: Pricing)

Management Issues[edit | edit source]

Measuring good management practices[edit | edit source]

The World Management Survey Method incorporates a Likert interval scale of 1-5; where a score of 1 equates to ‘worst practice’ and a score of 5 equates to ‘best practice’. A score of 5 indicates firms are flexible and can easily adapt their way of conduct in order to bolster the overall organizational effectiveness and as well as individual productivity. Generally, when analyzing and measuring management practices, be wary of monitoring, targets and incentives. Monitoring looks at the internal structure and processes of the firm in relation to feedback and continued growth. Targets look at whether the firm is able to construct the right goals and is following through with the right actions in order to accomplish those goals. Lastly, look at whether the firm incorporates any form of incentives may it be intrinsic and extrinsic which can affect employee motivation and well-being.

World Management Survey Methodology[edit | edit source]

The World Management Survey was developed to measure management practices in order to investigate and explain the difference in management practices across firms and countries in different sectors. The methodology allowed interviewers to:

1. Obtain and conduct interviews by asking questions about management practices within the organisation

  • Scorecard for 18 monitoring, targets and incentive practices
  • Roughly 45-minute phone interview of manufacturing plant managers

2. Ensure the collection of accurate, comparable and unbiased responses through the use of a "double-blind" technique

  • Interviewers do not know the company's performance
  • Managers are not informed (in advance) they are scored
  • Run from London, with same training and country rotation

3. Get firms to participate in the interview to evaluate and score management practices by defining the practices as "good" and "bad" and ranking them from 1 (worst practice) to 5 (best practice)

  • Introduced as "Lean-manufacturing" interview, no financials
  • Official Endorsement: Bundesbank, PBC, CII & RBI, etc.
  • Run by 100+ MBAs (credible with business experience)

Core Managerial Practices[edit | edit source]

1. Operations Management (monitoring) Managers administrate the daily operations of the company in order to achieve efficiency and maximize the firm's revenue.

  • Use of lean techniques -

LEAN techniques are tools of management that avoid waste time in any process of the company.

1) Kaizen which is a Japanese word meaning continuous improvement. This technique has the purpose of improve work processes in different ways.
2)Poka-Yoke which is also known as "Mistake Proofing". This technique helps to prevent mistakes of defects during processes within the company.
3)Kanban which is a tool to prepare a schedule for production and to minimize work-in-process while encouraging improvement in other different areas. This tool includes a small stock point that sends a signal when products are out of stock by a downstream process. Kanban uses display cards to send the signs for movement of materials between every step of the production process. This tool was applied at Toyota in order to improve and keep a high level of production. Lean techniques are most effective when specialising the organisational structure of the firm and promoting an organisational culture. For example, each department has a head that they answer to, leaving a degree of anonymity within the department silo. Decisions will be more time-efficient and employees will have more of an education of the operations of the organisation. 
  • Reasons for adopting lean processes - Helps to improve efficiency, productivity and creation of smarter processes.

The main purpose of LEAN processes is maximize customer value and minimize waste within the company. In other words, create more value for customers but using fewer resources. Companies should focus on the key processes in order to increase customer value. To achieve this, lean techniques focus on optimizing the flow of products/services through value streams across technologies, assets, departments and customers.

2. Performance Monitoring (monitoring) It consists in monitoring that employees are working towards the company's goals and objectives.

  • Process documentation
  • Use of key performance indicators (KPI) - it measures how the company is achieving the key business objectives efficiently. This helps companies to evaluate their success when they want to reach a target. The high levels of KPIs may be focused on the business's overall performance and the low levels of KPIs may be focused on the performance of departments within the company (sales, marketing, customer service, human resources, etc).
  • KPI reviews
  • Discussion of results

Discussions of KPI results are imperative to ensure managers and employees are like-minded. [6] refers to the importance of using a managers own measures of success to help understand progress and grow as a business. Using KPI's and reviewing these goals creates transparency within the firm. PwC, a powerhouse in the banking sector, created a review documenting the need for financial and non-financial KPIs. This can only be achieved through reviewing and discussing the results of the firm in the previous financial period. The company analysed a gap in customer satisfaction and thus, introduced a non-financial KPI to reinstate the firms position on the energy sector as well as other major sectors the firm is involved in.

  • Consequences of missing targets


3. Target Setting (targets) It is a process where the company set targets for the long or short run. As a part of this process, there are some qualities that should be considered:

 1)Specificity - managers should be really specific when they establish a target. For example, if the company wants to raise capital. It is more specific 
 that the goal should be raise capital by $2 million in 2030. Then, the company has specific information of what they want to achieve and how to do it.
 2)Optimism - when managers are establishing targets, they should be positive and base their targets in the best case scenario.
 3)Realism - targets should be established step by step. For example, managers cannot set a target for expected revenue of $2billion if they have never 
 reached that target before. They need to begin with small steps and lower their targets with more realistic quantities.
 4)Short and Long term - managers need to differentiate the period of time for short term and long term goals in order to set realistic targets. Short 
 term includes days up to a year and long term includes 5, 10, 20 years, etc.
  • Choice of targets
  • Connection to strategy
  • Time horizon
  • Level of challenge
  • Clarity of goals and measurement

4. Talent Management (incentives)

  • Talent mindset at the highest levels
  • Stretch goals
  • Management of low performance
  • Talent development
  • Employee value proposition
  • Talent retention

Management Practices Differ across Firms and Countries[edit | edit source]

  1. Firms perceived to have better management will tend to grow faster, be more productive and larger potential growth helping them to survive through tough competition
  2. Countries (e.g. U.S.) that score high in overall management practices usually have very few badly managed firms, while the countries (e.g. Brazil, India) with a "long tail" of poorly managed companies tend to have low management scores.
  3. Culture varies across firms and countries, shaping their management styles leading to the different specialisation of management styles.
  4. A market with strong product competition if often associated with better management practices as they push incumbents to do better and badly managed firms will exit early due to the competition.
  5. Family-owned firms who appoint a family member to a position of management are badly-managed.
  6. Government-owned firms are managed badly. In contrast, firms owned by private firms are well managed.
  7. Multinational organisations tend to be better managed and will carry their management styles across countries to be adopted (e.g. a focus on targets, incentives, monitoring, etc.)
  8. Firms involve in exporting rather than producing their own goods to sell tend to have better management.
  9. Government owned corporations have poor management practices comparatively to privately owned corporations.
  10. Firms that utilises more educated worker as human capital tend to have better management practices.
  11. In broader terms, looking at a country level, less restrictive regulation in labour is associated with the better use of incentive by management, which could lead to "better" management due to higher incentives.

CEO overconfidence[edit | edit source]

Overconfidence bias has been widely researched by behavioural and financial economist James Montier. The findings of his experimentation can be applied to the understanding of CEOs and their overconfidence. The study completed by Montier found that CEOs tend to view their leadership and technical capability as average or above average, with 74% of test subjects reporting their individual performance as above average. The overconfidence bias underpinned by Montier may also explain CEO's beliefs in regard to the stock price, as it is commonly reported by CEOs that the company stock price should be higher than it currently is. There are a substantial amount of executives that exhibit overconfidence and do so by holding on to stock options until very close to their expirations.

Overconfidence bias is risky: Overconfidence bias can lead to risky and uneducated decision making among leaders and CEOs. As an example, CEOs may hold stock options very close to their expiration. It can also cause CEO's to miss systematic issues endemic to their company. In the case of Theranos for example, Elizabeth Holmes as CEO was extremely confident in her ability to deliver a method of testing blood samples quickly. Many claimed that her overconfidence was actually one of the companies strengths, but ultimately it meant that she missed several massive issues with her company and product.

Common Trends: Reported overconfidence in CEOs correlated with paying less in dividends and less reliance on external finance (equity based).

Identifying Overconfidence Bias:

  • Some firms may actually prefer a degree of overconfidence for the reason that they plan to make a change in strategy or vigorously pursue innovation. * Firms can offer overconfident CEOs a reduced amount of company stock as part of the compensation and remuneration packages.

In a study conducted by Malmendier and Tate (2008), they predicted that an overconfidence CEO would display a highly sensitive cash-flow in an equity-dependent firm. This is consistent with CEOs having a much higher tendency to pursue a merger, specifically diversifying deals with which they are able to obtain internal financing (Tate, 2015). With respect to the prediction, non-confident CEOs are likely to yield a positive market reaction of a merger announcement in comparison to overconfident CEOs. The chosen level of (external) investment opportunities are considered to be substandard for overconfident CEOs, however, further research needs to be conducted to assess the optimality of external investment.

The relationship between dividend policy and overconfident CEOs is illustrated through their choice of paying smaller dividends. Which is consistent with their intention of building slack their firm's future financial investment needs as overconfident CEO's view their external financing costly. Overconfident managers are less apprehensive about sourcing external capital, therefore, issuing less equity than their respective peers (more debt) and continual aversion to equity financing.

Additionally, overestimating future earnings is another common occurrence for overconfident CEOs to portray under their earnings management. They have a tendency to aggressively borrow more to avoid under-performing in the next earnings forecast, as they practice less conservative accounting principles. These accounting practices include delaying recognition of financial losses and overestimating revenue streams. There is often a higher emphasis on incentives to inaccurately state earnings for periods as overconfident CEOs demonstrate an optimistic bias. (Tate, 2015)[7]


CEO Overconfidence and Innovation[edit | edit source]

Previous literature and thought surrounding CEO overconfidence acknowledges that the presence of overly optimistic leaders in firms can lead to risky decision making and carelessness in forecasting, among other negative repercussions. In a seminal piece of literature investigating CEO overconfidence and corporate investment, Malmendier and Tate (2008) found that overconfident leaders had a tendency to destroy value through risky and unprofitable mergers and poor investment behaviour while Hribar and Yang (2011) concluded that overconfident CEOs were more likely to miss their earnings forecasts.

Taking business risks can lead to positive effects of innovation however and a pragmatic (while overly optimistic) CEO is often required for change to occur. This is because CEO's are often the final arbiter as to whether or not a company innovates. Galasso and Simcoe (2011) allude to precisely this and indicate that CEOs decide whether or not to innovate. It is therefore incumbent on the board of directors to decide which direction they want a CEO to take the company before hiring them.

Unsuccessful innovation often unfairly represents the talent of a particular CEO. Overconfident leaders underestimate the likelihood of not succeeding and are, therefore, the type of employees who are more likely to take risks. If a firm’s business goals are to pursue innovative processes, then this CEO is expected to be the best person for the job. Reducing the probability that a CEO will lose their job or be punished after a decline in profits while pursuing innovation is therefore likely to increase innovation. On the other side of this, if a firm is willing to offer substantial rewards for successful innovation then this will increase an overly optimistic leader’s propensity to innovate. They may dismiss potential negative effects that their overconfident behaviour has in regards to investment behaviour or other management decisions (Glasso and Simcoe, 2011; Aghion et al, 2009).

Galasso and Simcoe (2011) show that CEO overconfidence is likely to trigger a significant change in a firm’s innovation strategy. These leaders can take control and reinvigorate a stagnant organization by eradicating innovation blindness, which is described as the overestimation by a firm (or member/s of a firm) of it’s current innate abilities and the underestimation of the benefit of innovating their current processes for increased long-run benefit (Leonardi, 2011). This blindness can lead to stifled creativity and poor future economic growth.

By acting as the triggers of significant changes in innovation strategies around the world, over-confident CEOs have been shown to provide businesses with positive effects such as obtaining more citation-weighted patents (Galasso and Simcoe, 2011).

CEO Overconfidence[edit | edit source]

CEO overconfidence occurs when the chief executive of a company believes the firm’s stock price should be higher than it currently is. CEO overconfidence impacts investment decisions, strategic decision making, including mergers and acquisitions. CEO overconfidence can occur due to the perception, pressure and perks of being a CEO for a successful firm (Malmendier & Tate, 2015). The image of a CEO can impact their self-confidence and public persona, influencing strategic decision-making. A form of managerial bias, CEO overconfidence occurs when high executives are over ambitious, impacting corporate investment decisions and can result in bad investments and the loss of millions of company dollars. Overconfidence is generally results in over investment, when the firm has a multitude of internal funds, including cash and capital that the CEO deems to be over little value (Malmendier & Tate, 2015(. It can be difficult to ascertain whether over investment of poor decisions making was the result of overconfidence, as biases are difficult to empirically measure. Often the personal company stock option portfolio of the CEO is used to measure overconfidence. While rational CEOs would choose to exercise stock options in order for them to diversify, an overconfident CEO would more likely overvalue the performance of the firm and choose to hold the options to gain a profit from future appreciation (Malmendier & Tate, 2015).

Undervalued good management[edit | edit source]

Management practices often occur through a collective understanding among individuals within the firm. Even with competent and able managers, the common issue of failure to foster good practices could lead to potentially negative outcomes for the firm. Through the optimisation of resources and building on sophisticated capabilities, successful and effective management can be achieved, allowing the company to flourish and motivate employees.

Undervaluing good management means that management practices are thought to be of little importance. Instead, the focus is directed on other aspects of the organisation such as increasing capital gains and maximising shareholder value, without considering the importance of the role a manager plays in the company. An example of undervaluing good management is having false optimism; this occurs when a manager’s self-assessment reflects outstanding performance when the reality is the opposite. As a consequence, the manager is likely to be unwilling to embrace and effect change in the way the company is being managed, potentially leading to missed opportunities and better results. Successful management is also often overlooked because it may be too expensive and time-costly to redesign the way a manager is implementing their strategies, despite its ineffectiveness on the organisation. Research has shown that good management can be as costly as capital investments like buildings and equipment, and as most organisations are allocated a tight budget, it is unlikely that they would spend on intangible resources such as refining their management practices. It is therefore essential that a firm applies centralised decision making processes when it needs to focus the entirety of the firm on a few or even one key issue. When there is danger to the survival of the firm, granting individuals adsolute decision making power allows them to lead far more decisively, decreasing the time wasted on collaborative decision-making.

Disregarding the importance of good management practices can also lead to lower profitability, lower productivity, slower growth rates, and may decrease the company's lifespan. At its peak, having good management is a competitive advantage. It is imperative as it provides current employees and managers with enthusiasm and the drive to stimulate and achieve organisational growth. Further, it also reveals to investors that the organisation is reliable and credible. Good management should never be underestimated and should always be a top priority for the organisation to exploit any possible opportunities.

Leadership and Business Decision Making[edit | edit source]

What makes a good leader? Good leaders/managers procure and hire capable individuals who thrive in a team environment and collaborate with those around them. One measure of a good leader is how quickly they can make themselves redundant over time by creating mechanisms which deal with a given problem in the same way that they would, without requiring their attention. This frees up the leaders attention for other, larger projects, and strengthens the business over time as more of these mechanisms are put into place.

What makes a good business decision? Good business decisions obviously need to be well thought-out, most likely be profitable and should definitely seek to better the organisation in one way or another. But mostly, good business decisions should be ethical, set an example for the rest of the business, demonstrate the core values and beliefs of the organisation and should ensure everyone involved in the process can be clearly accountable for their actions and sometimes even their inaction.

Centralised Decision Making[edit | edit source]

There are many advantages of highly centralised decision-making processes. These processes can create clear responsibility, clear decision making and increase the importance of central figures within the organisation. Over time, a process called accretion can occur within a business whereby the complexity of a business can increase over time as new systems are added to it. The constant layering of new systems can render certain aspects of the business extremely hard to navigate and even non-functional. Untangling this complexity through normal processes is often impossible, and can benefit greatly from centralised decision-making. In dire times for firms, a highly centralised decision-making structure will create the greatest benefit for the business as it allows agents within the company to cut through existing structures within the business. The disadvantages are that it may discourage accountability, stem the flow of information, create inefficient management of both physical and human resources and blur the businesses ability to procure effective resources with what is required. Under centralised decision-making, the business must often tighten its focus to solve a few major problems. This can mean that the business becomes blind to other threats and opportunities. It also means that the business is less dynamic with the reduced independence of its employees.

Collaborative Decision Making[edit | edit source]

Collaboration in the organisational decision-making process involves using both divergent and convergent ideas in a group setting to realise the most effective way forward. In a collaborative decision-making environment, teams are made up of people directly involved in the process and therefore can make decisions with all the relevant information in front of them. Further, teams made up of individuals with various skills and assets can lead to increased innovation and can make information disseminate throughout the organisation faster. As people with differing functional expertise work together, potential risk also becomes easier to see and allows for seamless integration with potential customer feedback and future direction of the organisation. Although using this process may increase the time needed to make the appropriate decision, the higher initial investment cost will be diminished by the benefit of more efficient and profitable decisions for the future. Thus, for this process to work efficiently and effectively, tensions between the team and management should be managed thoughtfully.

Process of collaborative decision making[edit | edit source]

A collaborative decision process involves 3 stages.

1. Discovery A team works on the decision to be made under some preliminary assumptions without being extremely detailed. The team decides if the hypothesis represents the underlying problem, or if it is merely a symptom of the true problem. The team will then assess whether or no the goals for the decision are achievable within the suggested timeline given the constraints. At the end of the discovery stage, the management will receive a. feedback about the problem from the team. b. The list of risk associated with the problem-decision. c. The possibility of delivering the requirement within the given constraints and d. will have enough information to make more informed decisions.

2. Inception Collaboration with consumers/users is essential in the inception stage. This stage exist to properly understand what the given problem or opportunity is. This stage requires more detailed work compared to the discovery stage as the team dives more in depth into boundaries of the problem and the causality it constitutes. The inception stage should help management yield enough information to prioritise the requirements, better understand the constraints and risks, and get a clearer picture of what and when to expect things. After this stage, the team needs less micro-management and has higher accountability, as more people are involved in the decision making process.

3. Iterative delivery This stage requires the constant collaboration with consumers to gain continuous feedback. These feedbacks are then analyse to form a consumer intelligence data that will assist managers to make more informed decisions in the future.

Nonstandard Preferences[edit | edit source]

There are two fundamental ingredients to a strategic decision: 1. Risk preferences 2. Time Preferences[8]

These preferences provide an indication of the decisions makers attitude towards how they rank risk alternatives along with whether they are impatient or patient in regards to time. [8]An example of when these preferences are considered important in decision making is when determining whether a project should be approved or rejected as future cash flows are continually uncertain.

However, when evaluating gains and losses its not always simple and there are multiple factors that affect how a scenario is evaluated. Three factors in particular include:

  1. 1. Exploitation Bias

Refers to the tendency of strategic decision makers to underinvest in long-term growth opportunities and to focus narrowly on short term opportunities. The reason being is when a firm has been successful and achieved positive growth executives find no reason for changing what has been working for them. Therefore, as a result of this bias new entrants are more likely to be drivers for new innovation rather than incumbent firms who tend to overinvest in exploitation instead of exploration. [8]

  1. 2. Endowment Effect

In an ideal world someone’s willingness to pay should be equal to their willingness to accept, however, not everyone looks at life from a rational perspective. There is a tendency when selling an asset to ask for more than what they are willing to purchase the asset for; this is called the endowment affect. [8] An example of this is an individual purchasing a good for $12, and, after a few years, another person offers $25 for that good. A rational person would see a $13 reward, even higher when time value on the good is taken into account, and accept. However, due to emotional bias, an individual might refuses the offer as they place a higher value on their owned object, over the market value they initially bought it for.

  1. 3. Extremeness Aversion

Executives can adjust their preferences and consider alternative resources by investing in options that from a value perspective are unable to be justified. Executives choose an intermediate option and steer clear of extreme options. Executives tend to fall for extremeness aversion when decisions are made in regards to a firms assets. Regardless of an assets value, resources that have a value closer to the status quo are more likely to have funds allocated to them.[8]

Summary[edit | edit source]

The below section aims at briefly summarising the key take aways from this topic:

Prospect Theory: How a person evaluates a risky prospect in relation to some status quo position. This can be split into two parts:

  • Probability Misconception: When there are big differences in probabilities of events occur, people are more likely to misjudge and have bias. E.g. overestimate the risk of dying from a shark vs. dying from smoking.
  • Loss Aversion: Losses are given more weight than gains in choices that have both gains and losses. People are more scared of losing $2 vs. winning $5.

Reflection Around a Reference Point: When all options are gains people are usually risk adverse but when all options are losses people are usually risk loving

Measuring Good Management Practises:

  1. Management Questions: 18 monitoring, targets and incentives – approx. 45min interview to plant managers.
  2. Obtaining unbiased comparable responses – interviewers do not know the company’s performance and managers are not informed they are being scored.
  3. Getting firms to participate in an interview - effectively a meeting where no financials are discussed, just how to run a lean manufacturing business.

Core Management Practices:

  1. Operations management – use of lean techniques along with the reasoning for doing so.
  2. Performance Monitoring – KPIs used and discussion of results, as well as ramifications if not met.
  3. Target Setting – choice of target and clarity around the purpose of said target.
  4. Talent Management – talent retention and development.

Firms with better management practises are larger, more productive, grow faster and have higher survival rates. These practises are more common in highly competitive industries. Firms that are inherited or government owned are generally poorly managed. Those that produce domestically and export internationally, those that that use more human capital (skilled workers), and those that are in countries with some labour market regulations. Multinationals are all generally well managed however do better than others in certain respects. American’s provide better incentives however the Swedish have better monitoring techniques.

Reference/s[edit | edit source]

  1. Investopedia. (2019). Expected Utility Definition. [online] Available at: https://www.investopedia.com/terms/e/expectedutility.asp [Accessed 18 Oct. 2019]
  2. 2.0 2.1 McGuigan, J, Moyer, C, & Harris, F 2013, Managerial Economics: Applications, Strategies and Tactics, Cengage Learning, Andover. Available from: ProQuest Ebook Central. [17 October 2019].
  3. Tversky, A., & Kahneman, D. (1992). ADVANCES IN PROSPECT-THEORY - CUMULATIVE REPRESENTATION OF UNCERTAINTY. Journal Of Risk And Uncertainty, 5(4), 297-323.
  4. Barberis, N. (2013). Thirty Years of Prospect Theory in Economics: A Review and Assessment. Journal of Economic Perspectives, 27(1), 173-196.
  5. Amos Tversky; Daniel Kahneman (1974), Judgment under Uncertainty: Heuristics and Biases, Science, New Series, Vol. 185, No. 4157. (Sep. 27, 1974), pp. 1124-1131
  6. Hurst, Roger (2018). "Guide to key performance indicators"
  7. Tate, U. M. (2015). Behavioral CEOs: The Role of Managerial Overconfidence. Journal of Economic Perspectives, 37-60.
  8. 8.0 8.1 8.2 8.3 8.4 Garbuio, M., Lovallo, D. & Ketenciouglu, E., 2013. Behavioral Economics and Strategic Decision Making. The Oxford Handbook of Managerial Economics, pp.The Oxford Handbook of Managerial Economics. Cite error: Invalid <ref> tag; name "name" defined multiple times with different content Cite error: Invalid <ref> tag; name "name" defined multiple times with different content