“Pay-for-performance” or “incentive pay” has been a top-of-mind topic for me in recent months. It’s a pretty pervasive industry (best?) practice, especially for executives and sales people, and many companies use it quite extensively beyond the bounds of those two functions. To develop a more first-principled point of view on this topic, I did some research aiming to understand the origins of the concept and the boundaries/contexts in which evidence suggest it may not be effective. I found several good resources, the most rigorous one was a paper by Ian Larkin, Lamar Pierce and Francesca Gino titled:

The Psychological Costs of Pay-for-Performance: Implications for the Strategic Compensation of Employees

But I’ve also used a slew of less rigorous resources, including:

• CultureAmp’s CEO explains why they’re not paying sales commissions (5 min talk) and his recent post on Remuneration and Culture
• Psychology Today: Why financial incentives don’t improve performance
• Inc: Why Sales commissions don’t work (in the long run)
• The Telegraph: Forget carrots and sticks, they don’t always work
• NYT: For some, paying sales commissions no longer makes sense
• Quartz: The dangers of sales commissions that work too well
• Think Growth: The best way to use money as a motivator is to take the issue of money off the table

Definition

Pay-for-performance or incentive pay is the practice of tying additional compensation to the achievement of a well-defined, measurable outcome. As opposed to a more permanent, long-lasting compensation change like a promotion.

Common examples:

• Closing a sales deal
• Completing a project on time
• Hitting a certain target for a metric

Agency Theory: the origins of incentive pay

Incentive pay became popular with the rise of Agency Theory in the late 1970s.

Agency Theory is based on a few core assumptions about companies and employees. Specifically that companies seek to maximize profits by motivating employee effort and attracting more highly skilled employees, while minimizing salary costs. And that employees seek to maximize utility by increasing income while minimizing efforts.

Agency Theory also takes into account some information asymmetries in the dynamic between companies and employees that give employees an advantage over companies. Specifically, that employees know their own effort exertion (while companies have imperfect information) and that employees know their skill level (while companies have imperfect information).

Taking those assumptions and information asymmetries into account, Agency Theory suggests that companies overcome these asymmetries by providing incentives for employees to exert effort and self-select by skill level. For example, by offering a low guaranteed salary with a large performance element, a company can incentivize higher effort from all employees, but it can also attract and retain employees with high skills, while ‘sorting away’ those with low skills.

Insights from Agency Theory:

1. Employees work harder when their pay is based on performance.
2. Companies are more likely to use performance-based pay when they have less information about actual employee effort.
3. Companies are more likely to use performance-based pay when they have less information about employee skill level, and/or as employee skill level is more heterogeneous.
4. Companies are more likely to use team-based performance pay vs. individual-based pay when coordination across employees is important, when free riding is less likely, or when monitoring costs are low.

Research in Psychology and Decision Research

However, since the 1970s research in psychology and decision research have painted a more nuanced picture of the dynamic between companies and employees. Two elements in particular, social comparison and overconfidence play a pivotal role in that dynamic.

Social comparison theory introduces considerable costs associated with individual pay-for-performance systems, because it argues that people evaluate their own abilities and opinions in comparison to referent others. Generally, people seek and are affected by social comparisons with people who are similar to them gaining information about their own performance.

People also tend to be overconfident about their own abilities and too optimistic about their futures. Overconfidence is thought to take at least three forms:

1. People consistently express unwarranted subjective certainty in their personal and social predictions.
2. They commonly overestimate their own ability.
3. They tend to overestimate their ability relative to others.

People tend to be overconfident about their ability on tasks they perform very frequently, find easy, or are familiar with. Conversely, people tend to be underconfident on difficult tasks or those they seldom carry out. This tendency has strong implications for overconfidence in work settings, since work inherently involves tasks in which employees have strong domain expertise in.

The above refines the assumptions about companies and employees. Specifically, that Maximize profits for companies also requires minimizing non-wage costs (counter-productive work behaviors), and that maximizing utility for employees also requires minimizing perceived inequality.

The information asymmetries should also be refined to take into account the fact that employees perception of their effort and skill level are biased (while companies have imperfect information).

Insights from Psychology and Decision Research: 

1. Perceived inequity through wage comparison, compounded by overconfidence bias, reduces the effort benefits of individual pay-for-performance compensation systems.
2. Perceived inequity through wage comparison, compounded by overconfidence bias, introduces additional costs from sabotage and attrition in individual pay-for performance compensation systems.
3. Perceived inequity arising through random shocks in pay (economic downturn, weather, client going bankrupt) introduces additional costs from effort, sabotage, and attrition in individual pay-for-performance compensation systems
4. Overconfidence bias reduces the sorting benefits of individual pay-for-performance compensation systems (low skill employees, will still self-select into a pay-for-performance scheme)

Alternatives to individual pay-for-performance

After incorporating insights from psychology and decision research, individual pay-for-performance seems less like the holy grail that Agency Theory made it to be. Are there better alternatives? Larkin, Pierce & Gino looked at a couple:

• Team-based compensation: additional compensation is tied to the achievement of team goals/objectives and is shared among the team members.
• Scaled wages: employees are compensated in relatively tight ‘bands’ based largely on seniority.

And concluded that:

1. Team-based compensation reduces costs of social comparison when individual contribution is not highly heterogeneous within the team.
2. Team-based compensation only resolves problems of overconfidence in individual pay-for-performance systems if the actual contribution of teammates is observable.
3. Scaled wages have lower social comparison costs than team-based and individual-based compensation systems.
4. Scale wages reduce costs of overconfidence in individual- and team-based pay-for-performance.

In Conclusion

Just like many other issues pertaining to the complex problem of human collaboration the answer to whether pay-for-performance is effective, is not a definitive “yes” or “no”, but a more nuanced one, depending on the specific context in which pay-for-performance is used.

Pay-for-performance will be more effective if:

• Work requires low cognitive load
• Outcomes are very controllable (effort and outcome are highly correlated)
• Outcomes are easily attributable – it is easy to separate out individual contributions which led to a certain outcome
• Overconfidence bias is minimal or non-existent
• Social comparison is limited or non-existent
• Global optimum can easily be decomposed to pre-set individual outcomes

Pay-for-performance will be less effective if:

• Work requires high cognitive load
• Outcomes are not very controllable (effort and outcome are loosely correlated)
• Outcomes are difficult to attribute
• Overconfidence bias is meaningful
• Social comparison exists
• Global optimum cannot be easily decomposed to pre-set individual outcomes

A wonderful piece by Charles Davies called “Why Your Purpose is a What not a Why”

Charles makes a pretty compelling case of getting rid of a hefty portion of business jargon captured in terms like: mission, vision, goal, outcomes, etc. and replace them with one simple word: What

They all attempt to capture the same thing: what we do. The only thing that changes is the timeframe we’re referring to. Two additional terms allows us to traverse various timeframes: Why (to what end?) expands the timeframe, and How (by what means?) shrinks it.

You can navigate the stack from any starting point moving either up (longer timeframe) by asking “Why?” or down (shorter timeframe) by asking “How?”.

An example from my current domain, demonstrating the edge cases:

What: Enable all children to reach to reach their full potential

How: By making the best education the most affordable one

How: By creating a networked school system with a strong network effect

How: By building a digital platform which enables progressive education practices

How: By creating a capability for educators to perform in-line, competency-based assessments (rather than rely on standardized tests)

How: By building a feature that enables an educator to capture a student’s work in real-time

…

What: Build a feature that enables an educator to capture a student’s work in real time

Why: To create a capability for educator to perform in-line, competency-based assessments (rather than rely on standardized tests)

Why: To build a digital platform that which enables progressive education practices

Why: To create a networked school system with a strong network effect

Why: To make the best education the most affordable one

Why: To enable all children to reach their full potential

First came the “I have a great idea” startups. A heroic founder will come up with a great idea on how to go about solving a particular problem. A few years and a few millions of dollar later, the team emerges with a product, only to find out that nobody thinks their product really solves the problem; or even worse — that nobody thinks that the problem they have attempted to solve is a real one.

Then came the Lean Startup movement, fully embracing the fact that a startup is an organization meant to search for a sustainable business model, and the best way to do so, is through a disciplined application of the scientific method:

In a nutshell, a startup is a hypothesis testing machine.

While a massive step in the right direction, I believe it is an insufficient one, since the methodology ignores a critical ingredient in this process. To extend the machine metaphor a little further: similar to other machines, the quality of the output (validated/invalidated hypothesis) is not just a factor of the quality of the machine, but also of the quality of the inputs (hypothesis formulated). If you’re making coffee with the best espresso machine out there, but using low quality coffee beans — you’re still going to get bad coffee. The quality of the coffee (output) is constrained by the quality of the beans (input). Or to use a different metaphor: you’re still throwing darts at the dart board turned-around and blindfolded, you just gotten very good at throwing the darts quickly and lifting the blindfold after every throw to see if you’ve hit your mark.

Talking to your customers is the simplistic solution to this problem. Your customers can be incredibly useful in helping you validate your problem hypothesis (after all, it’s their problem you’re trying to solve), and on rare occasions, they can also help you formulate a better problem hypothesis. But more often than not, they will not be able to help you in formulating your solution hypothesis. Just because you have the problem, doesn’t mean that you have any idea how to solve it. To use an intentionally extreme example: just because you have cancer, doesn’t mean that you can help me find what might be the cure. This logic also applies for the people who are part of the startup: just because you suffer from the same problem you’re trying to solve, doesn’t necessarily make you any better than your (future) customers in formulating hypotheses around solutions that might work. Sure, you may get lucky in your guesses, but there’s a better way.

So what is that missing ingredient? I’d argue that it’s true mastery in the problem domain. Deep understanding of the root causes behind the problem, what’s already been tried and worked/didn’t work and under which circumstances, where the ecosystem as a whole is heading and why, etc. You get the point. And if you, dear founder, are not the master of the problem domain in which you operate — find someone who is and get them on your team. Not as a board member. Not as a part-time adviser. But as a full-time member of the team, in there with you, in the trenches, informing and refining your hypothesis testing machine on a daily basis. This is, in my opinion, one of the most critical ways to de-risk your search for a sustainable business model, and one that is well worth investing in.

It’s time to move past Lean Startups, and start moving towards Domain Mastery Startups.

Unlocking human potential — proactive practices for individual elasticity by Deloitte Center for the Edge

As the punny name might suggest, the “center for the edge” is not your typical Deloitte business unit. As big corporation skeptic, I found their research, specifically on future of work and human potential topics, to be surprisingly progressive and though-provoking. This piece is no different. Once you peel off the over-frameworky-ness (pretty sure this is not a word), you end up with a pretty powerful idea.

The core thesis

If we take as our goal sustainable, long term performance, the practices of slowing down and speeding up can be seen as complementary rather than contradictory.

We nourish our minds, spirit, and bodies through growth and exploration as well as through rest. When both sides are understood to be complementary and pursued deliberately to reinforce each other, the effect on unlocking potential is greater.

Roots and shoots

They define two type of practices, roots and shoots, and make the case that by adopting both and going through cycles of learning and unlearning, an individual can discover purpose and passion.

Roots

Entail slowing down and making space to discover and connect with the fundamental values that drive us. Roots can counter increasing stress and make us more open to exploration. A set of practices for connecting to our roots can can provide a foundation for speeding up.

• Rest — taking time to pause — not only as a stopgap or a reaction to a constant pressure, but also, to break the busyness cycle and as an antidote to the increasing inefficiency of incremental work… Rest allows us to replenish and rejuvenate, which in turn allows us to be more effective, creative curious and resourceful in our efforts.
• Reconnect — connecting to our core values and principles provides a touchpoint and affirmation of what truly matters. The communities that surround us provide safety and stability, as well as avenues for dialogue, reflection and reframing.
• Reflect and reframe — in times of rapid change the past may no longer predict the future. The very things that made an individual successful may actually be his or her downfall when underlying assumptions and contexts fundamentally shift. In such times, we need practices that help us adopt new perspectives to see what is no longer working, and that help us muster the courage for unlearning old patterns and learning new ones.

Shoots

Feed creativity and empathy, can stoke commitment and a sense of purpose, and involve exploring, expanding, and accelerating learning. Speeding up in the absence of rest, reconnection and reflection will likely lead to temporary, isolated learning.

• Act — by participating in flows and gather data and experiences, we learn about ourselves and our environment. Being proactive and deliberate rather than reactive is a key differentiator of actions in this context
• Amplify and accelerate — isn’t about doing more or staying busy. Instead, it means deliberately looking for relevant flows in order to connect with more people in areas related to one’s goals, get more data and feedback, and learn more rapidly about ways to have more impact
• Adjust and align — learning cycles need to be rapid and iterative. With each exploratory action, in the shoots, the experience and information gathered is used to assess and adjust the short term learning focus.

Meta-practices

Roots and shoots practices tend to map into one of six critical objectives (meta-practices) that are aimed at feeding and sustaining the learning cycle.

Roots:

1. Explore core values — proactive explore what lies beneath the surface:

• Practice deep introspection
• Journal and tell stories
• Engage in deep discourse

2. Replenish and re-energize — take time to slow down and fight the epidemic of constant “busyness”

• Practice mindfulness
• Engage in energy and attention management
• Implement digital detox
• Say “no” — conscious decision to forego some potential opportunities

3. Cultivate community — seek external validation of reflection and reframing

• Join communities of interest
• Join communities of practice

Shoots:

1. Shape serendipity — make space for the unplanned to surprise you

• Take a sabbatical
• Plan open time in your schedule
• Say “yes” to random opportunities that are outside the norm

2. Explore edges — cultivate a sense of curiosity, possibility and imagination

• Maintain a broad social network
• Explore new topics

3. Be uncomfortable — expand your comfort range — emotionally, mentally, and physically — and cultivate a growth mindset and a beginner’s perspective

• Practice failing
• Shift parameters/repackage risks
• Focus on process and practice, not outcome
• Undergo voluntary physical discomfort

Senior leadership teams: what it takes to make them great by Ruth Wageman

What makes teams of leaders leadable? by Ruth Wagerman and Richard Hackman

Definition of leadership team

A leadership team is a group of individuals, each of whom has personal responsibility for leading some part of an organization, who are inter-dependent for the purpose of providing overall leadership to a larger enterprise… members of such team also have a collective responsibility for aligning the various parts of the organization into a coherent whole and fostering its overall effectiveness.

The 4 types of leadership teams

1.  Information sharing (alignment) teams — these teams exchange information about various organizational matters and bring together in one place external intelligence that may be useful to other parts of the organization or to the enterprise as a whole. They also hear about direction and initiatives from the team leader, which helps make the individual leaders on the team better informed, better aligned and more able to do their individual jobs well.
2. Consultative teams — aim to make the team leader better informed and better able to make his or her own decisions. In contrast to informational teams, consultative teams actively debate key issues, giving members the chance to learn from one another — but the final call is made by the team leader.
3. Coordinating teams — are those whose members come together to coordinate their leadership activities as they execute strategically important initiatives. Members of coordinating teams are highly interdependent, have shared responsibilities, and must work together frequently and flexibly to accomplish their shared purpose. Coordinating teams also serve info-sharing and consultative functions.
4. Decision making teams — make the small number of critical decisions that are most consequential for the enterprise as a whole.

While these types are listed in order of growing value to the organization, without proactive management to the leadership team as an entity, there’s a natural drift in the opposite direction (from “decision making” to “info sharing”).

The ironic features of leadership teams

1.  Leader teams are composed of powerful people — yet they tend to be undersigned, under-led and under-resourced.
2. Membership is important and coveted — but members often don’t know who is on the team, and they do not really want to come to team meetings.
3. Members are overloaded — but they tend to waste enormous amount of time in team meetings.
4. Authority dynamics pervade leadership teams and complicate team process — but members won’t talk about them.

The 3rd irony in particular deserves a deeper look. Leadership teams waste their time in three ways:

• They focus on surprisingly trivial matters. They do make decisions together but often about issues that are not consequential for the team’s core leadership work.
• When they do address important matters, leadership teams tend to become caught up in seemingly irresolvable conflicts. Conflicts in senior team often stem from members’ views that their main responsibilities are to maximize the effectiveness of the unit they lead.
• They cut short potentially vital discussions by agreeing to disagree and then moving on.

The 6 enabling conditions for an effective leadership team

1. A real team

• Bounded — clear who is — and who is not — on the leadership team.
• Stable — membership is kept in tact for some period of time.
• Interdependent — members share accountability for a common purpose.

2. A compelling purpose

• Clear — can imagine what it would look like if we achieved it.
• Challenging — a stretch of capability to achieve it, but not impossible.
• Consequential — important impact on the success of the organization and on the lives and work of others.

3. The right people

• Members are people who can take an enterprise perspective.
• Members have the ability to work collaboratively.
• All the “derailers” are removed — those who undermine others, bring out the worst in others, exhibit lack of integrity, are unable to see other’s perspectives.

4. Solid structure

• Right size — keep it small.
• Meaningful team tasks — the work members do together is vital and connected to the strategy.
• Norms of conduct — members understand what must always be done, what must never be done.

5. A supportive context

Rewards do not themselves create collaboration, they can be a powerful negative; they can divide (status, fairness).

• Information — what data the team needs — in a form they can use.
• Education — training and technical consultation to build expertise.
• Material resources — the space, time and “stuff” for working together on hard decisions.

6. Expert team coaching

• Team coaching — as an entity.
• Coaching and participating is often too hard — consider an external coach.
• Demand of yourself the same work ethic about leading the team as you would have about every other professional responsibility.