Week 6 - Strategic Decision Making

Question 1

Barber et al (2019)

Answer 1

• 1 in 5 top 100 US firms had a major stumble in the past decade (Intel, Nokia, Target)
• Stumble: underperforming the market by at least 25%
• Two main causes of stumbles:
• Failed strategy: companies faced low growth, patent expiry, or disruptive competition
  o Failed innovation or repositioning
  o Overinvesting in revenue growth in the core business, e.g. Tesco investing in out-of-town superstores in the UK while online shopping was on the rise
  o Risky diversification, e.g. BT telecom company tried diversifying into global IT services and compete against IBM by several acquisitions
  o Outsized acquisition
  o Portfolio breakup
• Operational errors:
  o failed to meet compliance requirements
  o poor financial risk controls, e.g. Investing in mortgage-backed securities carelessly during the financial crisis – Citigroup, Morgan Stanley, UBS
  o efficiency problems
• Two more mistakes resulting in a stumble:
  o Top decision makers and organisational capabilities are poorly matched to the company’s strategic challenge
  o Top decision makers are prone to decision-making biases when assessing risks
• Management problems:
  o Bias to growth
  o Bias to assessing compliance and financial risk
• Emperor’s Clothes Test - CEO is suitable if there is appropriate:
  o industry background
  o line management experience
  o relevant education and career background for addressing strategic challenges

Question 2

Leiblein et al (2018)

Answer 2

• The field of strategy should be defined by the study of strategic decisions that are interdependent with other decisions, so they can guide other decisions and result in choice patterns
• Strategic decisions: those with sharp trade-offs or decision tensions or those decisions that are highly interdependent with other decisions; they lie within the context of complex systems and several other actors
• Methods of evaluating strategic decisions:
  o Resource allocation ‘assessment’: NPV>0, assessing risk to return etc
  o Competitive advantage ‘assessment’: qualitative, entrepreneurial process
  o Theory of the firm: transaction costs

Question 3

Lindebaum et al (2020)

Answer 3

• AI algorithms: supercarriers of formal rationality as they can learn and improve outcomes by the expansion of their dataset – enhanced decision-making capability
• However, it might reduce substantive rationality – human’s capacity for value-rational reflection and action – empirical, moral, and aesthetic terms as well
• Formal rationality denies randomness, is universal and is not based on qualities of the individuals concerned (specific conditions and the decisionmaker)
• Substantive rationality can be formalised, which can lead to algorithmic decisions
• However, this leaves out uniqueness, process, and time – e.g. strategic planning might in itself lead to heightened costs
  Formal decision-making relies heavily on assumptions and it has many limitations as data is not complete, so we need substantial rationality

Question 4

Sibony et al (2017)

Answer 4

• Obstacle: behavioural strategy focuses on addressing individuals cognitive biases – however, it is extremely hard to individually assess and change these – people unconsciously do not want to lose these biases
• The solution is to design decision processes on the level of the enterprise
• Mintzberg: decisions are strategic when “important in terms of the actions taken, the resources committed, or the precedents set”
• Decision processes can dampen individual biases; e.g. capital expenditure that requires approval on several levels dampen familiarity bias and favouritism
• They can also create biases; e.g. many layers of decision approval could lead to collective degree of risk aversion
• Strategic decision processes:
  o Investment processes: variable is the degree of risk
   Risk-taker: one-off investments into ‘managerial ego’
   Risk-aversion: attached to current resources, fails to consider existing portfolio diversification for one-off decisions
  o Resource allocation processes: variable is agility – achieving the desired level of change and reallocation
   Unconsciously anchoring to past figures, to status quo
  o Blue sky processes: variable is innovation – organisational processes that achieve the desired level of novelty or creativity
   Availability bias: repeating past successes
   Hot stove effect: avoiding past failures
   Analogies to similar organisations
   Halo effect: follow successful examples
   So, decisions are reduced to 2-3 familiar processes
• Levers for designing strategic decision-making processes:
  o Formality of the decision-making process
  o Layers of approvals
  o Setting the amount of information needed
  o Rules around participation/representation
  o Accounting for contrasting incentives
  o Debate as design variable
  o Setting the formal closure of decisions: who has the final word?

Question 5

Trunk et al (2020)

Answer 5

• “Researchers agree that AI can be used for the collection, interpretation, evaluation, and sharing of information, thereby providing support in speed, amount, diversity, and availability of data”
• Humans are needed to ensure the quality of information and interpretation – data is often historical, not relevant enough
• First step: specify the reason for integrating AI and the resulting decision tasks to be supported
• AI literacy is crucial for proper use and efficiency – training required

Question 6

Chatterjee & Hambrick (2007)

Answer 6

• Narcissism in CEOs is positively related to strategic dynamism and grandiosity, as well as the number and size of acquisitions, leading to extreme and fluctuating organisational performance (in the computer hardware and software industries)
• Narcissistic CEOs favour actions that attract attention, resulting in big wins or losses
• However, their firms’ performance is generally no better or worse than firms with non-narcissistic CEOs

Question 7

Kahneman (1993)

Answer 7

• Decision-makers are excessively prone to treat problems as unique, neglecting both statistics of the past and the multiple opportunities of the future
• This leads to isolation errors:
  o Forecasts of future outcomes are often anchored on plans and scenarios of success rather than on past results – overly optimistic
  o Evaluation of single risky prospects neglect the possibility of pooling risks – overly timid decisions
• Instead of accepting risks and focusing on expected outcomes, CEOs try to overcome risks (like challenges) – they want to be in control
• Individual preferences for risky prospects:
  o Risk aversion: prospect theory – loss aversion

o Near-Proportionality: risk aversion is present with small gambles as well, which is unreasonable given that they do not raise the issue of survival, and statistical aggregation often makes them individually irrelevant
o Narrow decision frames: people consider decisions one at a time – risk seeking when less, risk aversion when gain
* Costs of isolation: considering gambles in isolation costs 27% of their expected value
* Managerial implications:
o Certainty bias – underweighting the probable gains in contrast to the sure ones
o Status quo bias when pressures of accountability and responsibility
o Managerial performance evaluation is biased as well – losses are overvalued + performance is evaluated over a short period
o Fragmented decision-making results in everyone individually acting in a risk-averse way – individual consequences
* Inside view: forecasting based on the circumstances of a project
* Outside view: looking at statistics of similar projects – often more realistic prediction
* Still, the outside view is considered ‘impersonal” and is rejected
* The inside view is susceptible of the fallacies of the scenario – e.g. entrepreneurs
* Optimistic bias:
o Unrealistically positive self-evaluations
o Unrealistic optimism about future events and plans
o Illusion of control
* Combining the problems: ‘risk can be modified by managerial control and skill’
* A project with the best forecasted outcomes has the most chance of being overly optimistic – regression to the mean – hard to control for these in an organisation
* Constructions nearly always cost at least 20% more than the pessimistic forecasts – still, we trust the forecasts
* Challenger disaster – “law of increasing optimism”
* However, sticking to realism might not be as good for an organisation as it seems either

Question 8

Starbuck and Milliken (1988)

Answer 8

Past successes result in the alteration of beliefs of decision-makers and the disvaluation of probabilities
1986 Challenger disaster - fine-tuning, inconsistent goals
The system developed false confidence in itself - failures were regarded anomalies, they got used to the risk
General trust in the brand (NASA) reduced concerns
Success can make a success less likely as it fosters confidence, inattention and routinisation
Failure motivates people to search for better solutions

Question 9

Weick (1993)

Answer 9

The collapse of sencemaking during the Mann Gulch Disaster
Constant communication creates cohesion and mutual trust
Shared meanings lead to strong frameworks and structure
close ties lead to clearer thinking under pressure
Team-building also builds resilience to sudden disasters
Managerial capitalism - decisions are made by managers instead of owners