Innovation Investment Decision Risk Aversion

If overcoming uncertainty and risk is the largest barrier to innovation regardless of the type of firm it is important to understand how human biases and tendencies may be influencing innovation investment decision making. Extreme leadership risk aversion can cause firms to miss good opportunities and harm long run performance and growth prospects. Extreme leadership risk taking on the other hand can bet the farm jeopardizing the entire business. Healthy leadership risk management requires a balance suitable for a firm’s external, internal environment, and business cycle. Canadian business leaders appear to be tending towards extreme risk aversion.

What are some people biases and tendencies that may be driving extreme leadership risk aversion when making innovation investment decisions?

Innovation Investment Risk Sources and Assessment

To understand the people biases and tendencies impacting innovation investment decision risks assessments we first need to quickly review innovation risks assessment.

The return on innovation investments targeting new or improved ways of doing business or products are measured in terms of new value creation or improved value capture. In pure financial terms returns would need to exceed the corporate hurdle rate within the business risk tolerance. Risk assessments for innovation investment therefore consider what could impact achieving the return on innovation investment. Although risk sources tend to be viewed as negative equal balance should be given to upside as well as down side risks.

In general business risk sources that may impact innovation investments can cover operations, market, financial, or political risks including:

  1. Market Risks – Risks impacting revenue forecast and demand for the innovation particularly assumptions related to customer’s need and willingness to pay for the innovation.
  2. Adoption Risks – Risks impacting the adoption of the innovation in the innovation ecosystem up to and including the end user.
  3. Technical Risks – Risks impacting the technical achievement of the innovation performance such as product performance, feasibility (readiness for commercialization), regulatory approval/compliance,
  4. Operational or Execution Risks – Risks impacting the operational delivery of the innovation including supply chain disruptions, procedural failure, cash flow for required work in process.
  5. Co-Innovation Risks – Risks impacting key supplier/partner innovation required to achieve  innovation or deliver the innovation to market.
  6. Schedule Risks – Risks impacting lateness of delivering innovation to capture the value.
  7. Cost Risks – Risks impacting the innovation investment costs that impact profitability.
  8. Quality Risks – Risks impacting the innovation investment customer satisfaction, conformance to specifications, reliability, durability, serviceability, and aesthetics factors.
  9. Financial Risks – Pricing, asset, currency, or liquidity risks impacting the innovation return.
  10. Reputation Risks – Risks impacting business reputation such as product failure, integrity, social systems,
  11. Legal Risks – Risks arising from the innovation that may drive liability torts, property damage, IP legal actions.
  12. Strategic Risks – Risks arising from the innovation that may create new threats from new market entries, strained partnerships, regulatory changes, random surprise events impacting innovation assumptions,
  13. Political Risks – Risks impacting market for the innovation arsing from government protectionism, freedom of trade and tariffs, labour markets, local capital markets, corruption, and openness in different countries.
  14. Business Model Risks – Risks that assumptions unpinning the innovation are valid.

The list of potential risks impacting innovation investments is long giving decision makers reason to pause.  Management competence & experience are required to judge the severity and probability of each risk. The context for innovation investment decision making is therefore complex, ambiguous, and subject to uncertainty.

Risk Aversion In Decision Making

How does the presence of risk affect decision making? According to Kahneman and Tversky most people are risk seeking when it comes to losses but risk averse when it comes to gains.  Instead of weighing decision alternatives impact on total value decision makers frame outcomes as either a gain or loss based on an arbitrary reference point.

For example framed as a gain if faced with two options:

Option A: Receiving $100,000 cash.

Option B: Playing a game that offers a 50% chance of winning $200,000 cash and a 50% chance of not winning anything.

Most decision makers would select the sure thing Option A (ie. risk aversion for a gain) even though the expected value of each is $100,000 (Option B expected value = $200,000 x 0.5 = $100,000). But if the decision was framed as a loss:

Option C: Paying $100,000 for an unexpected cost or expense.

Option D: Playing a game that offers a 50% chance of paying nothing and a 50% chance of paying $200,000.

If forced to decide individuals would choose Option D (ie. risk seeking for a loss) again even though the both have an expected value of $100,000.

This helps to explain why innovation decision makers will take risks on continuous improvement which they frame as loss but not on R&D projects or export trade options which they frame as a gain. Continuous improvement is seen as Option D whereas R&D projects/export trade options are seen as Option B. Canadian business leaders decisions tend towards Option A and D and avoid Options B and C. The ability to make decisions based on probability tends to be avoided unless decision makers have confidence in the business case numbers.

Individual Biases and Tendencies Influencing Innovation Investments

Participants in the innovation risk assessment process could include: idea generator, designer, engineer, project manager, support staff, marketer, sales, executive sponsor, intermediaries, partners, financiers, and finally the investment decision maker. What are some of the individual biases that can influence any of these participants and possibly cause them to be extremely risk averse:

Overconfidence Bias – Individuals tend to have unwarranted levels of confidence in their judgment, occurrence of positive events, and accuracy of forecasts and then the underestimates of the likelihood of negative events. This bias can cause innovators to identify hidden flaws in their assumptions, approaches, or estimates of cost or schedule. High prevalence of this bias may cause decision makers to discount claims from employees appearing as extreme risk averseness.

Status Quo Bias – The tendency for individuals to prefer to leave things as they are driven by an aversion to loss. (Samuelson & Zeckhauser) This bias can cause decision makers to prefer to continue to use the current process, existing product line or business model even though the competition or market is changing. This bias certainly drives extreme risk averse behaviour.

Availability heuristic – The more prevalent a category is judged to be the easier it is for individuals to bring instances of this category to mind. Recent public failures or events may be cited as possible reason not to proceed even though the connection to proposed innovation investment is not relevant. This tendency could undermine the credibility of innovators in the eyes of decision makers.

Base Rate Fallacy – People who consult their neighbors and friends will discount perfectly valid information and choose instead to rely on a vivid example. Flawed vivid examples could steer decision makers and innovators away from the best course of action or alternative. This tendency could undermine the credibility of innovators in the eyes of decision makers.

Herding Instinct – The tendency for individuals to follow the behaviour and opinions of others.(Belsky & Gilovich) Individuals may propose innovation initiatives that are similar to what competitors are doing rather than focusing on what the customer actually needs and wants. This tendency could cause decision makers to delay decisions if they see their competitors not following the herd which is not necessarily bad but if they only approve investments that follow the herd and the herd is not moving it could be interpreted as extreme risk averse behaviour.

Gambler’s Fallacy – The tendency to treat chance events as though they have a built-in evening-out mechanism even though each event is independently determined. Innovators may continue to experiment hoping for a desired end result rather than learning from the results that may point in a very different direction. This tendency could undermine the credibility of innovators in the eyes of decision makers.

Perseverance Effect – The tendency for people to continue to believe that something is true even though they are offered strong counter evidence that disproves or proves it to be false. (Ross & Lepper) This tendency could also blind innovators from learning from results and decision makers to avoid making investment decisions.

Illusory Correlation – Tendency to see invalid correlations between events.(Hamilton & Gifford) This tendency undermines the soundness of underlying rationale or logic of decisions. This tendency could undermine the credibility of innovators in the eyes of decision makers.

Anchor Bias – Tendency to make estimates on readily available evidence that is meaningless. This tendency could also lead innovators down the wrong path or blind them to possibilities. This tendency could undermine the credibility of innovators in the eyes of decision makers. This tendency could also constrain decision making.

Confirmation Bias – Tendency to use favourable information that supports a position and suppress information that contradicts the position. The underlying assumptions and data must be challenged and verified. This tendency could undermine the credibility of innovators in the eyes of decision makers.

Hindsight Bias – Tendency for individuals to infer a process once the outcome is known but unable to predict outcomes in advance…”I knew it all along”.(Fischhoff) The inability to connect the dots to create new value also constrains taking chances.

Functional Fixedness – Tendency to base a problem solution on familiar methods but hinders the development of strategies for new situations.(Adamson & Taylor) This tendency could lead decision makers to constrain their view of available courses of action.

Selective Recall – The tendency to recall only facts and experiences that support assumptions underpinning a position. This tendency could undermine the credibility of innovators in the eyes of decision makers.

Set Effect – Prior experience can have a negative effect on solving new problems by limiting an individuals view in breadth and generality. In the absence of experience and weak competition firms may avoid innovation investments based on limited prior experience.

Biased Interpretation – Individuals only hear what they want to hear particularly in the presence of ambiguity. This applies to misinformed, uninformed, or narrow thinking in strategy that constrains consideration of innovation before it has a chance to demonstrate its potential.

Curse of Knowledge – Individuals who are privy to information and knowledge that they know others are not continue to act as if the others have the information. Innovators seeking to persuade business leaders need to be aware that decision makers do not have the level of technical understanding.

Escalate Commitment – Tendency direct more resources to a failed course of action. This tendency is particularly prevalent in failed innovation projects where good money is thrown after bad based on continued results that contradict assumptions.

Mental Accounting – The tendency to treat money differently depending its source, where kept, and how the money is spent. Some money is spent freely while other money is highly scrutinized. (Thayer) This is an interesting tendency in decision makers particularly if they apply a very stringent standard on innovation investments but not other expenditures with lower potential return or expenditures susceptible to the status quo bias.

Organizational Biases Influencing Innovation Investments

Complex innovation investments often require teams or groups such as: innovation initiative team, product development team, production team, sales team, leadership team, and board. Organizational biases that can influence these teams or groups during innovation and to be extremely risk averse include:

False Consensus Effect – Most individuals think others agree with them more than the group actually does. This effect could give the impression that decision makers are risk averse but actually have better or different information from the team.

Groupthink – The tendency for group members not on side to fall in line and suppress their objections.(Janis) This tendency could undermine the credibility of team in the eyes of decision makers. In the case of a leadership team if members don’t state their views on potential opportunities then good opportunities may be missed.

Tunnel Vision – The tendency for a group to underestimate the number of feasible options available. Tunnel vision could lead to a course of inaction and risk aversion.

Uneven Participation – The tendency for a small number of strong willed individuals to do all the talking. Similar to groupthink.

Naïve Realism Principle – The tendency for people to expect others to hold views of the world similar to their own. This tendency can blind decision makers to good opportunities.

Dominant Bias On Innovation Investment Risk Aversion

Dominant bias and tendencies behind extreme risk aversion in Canada or in general may be the status quo bias, mental accounting, herding instinct, set effect, and biased interpretation. Certainly a predisposition to not take innovation ideas serious because of perceived credibility issues driven by some of the biases may also to blame.

Methods to address individual and organizational biases and tendencies when making innovation investment decisions will be the subject of future posts.

One thought on “Innovation Investment Decision Risk Aversion

  1. Pingback: The Capitalist’s Dilemma Explains A Lot | Alopex on Innovation

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