You may have noticed that what is new often behaves differently than what has become accepted over time, whether it is in a market, or a technology, or involves people and firms. Much research supports these general ideas, and this course builds on them to help you develop a perspective on managing innovation. That is, you will build your capability to lead and design your organization in effectively implementing innovation initiatives and achieving their strategic intent. To do this, you will learn a set of frameworks, tools, and concepts that can help you address several important challenges in managing innovation. The first challenge regards how to successfully implement innovation efforts within established firms and alongside established businesses. You then investigate the particulars of managing innovation when disruptive technologies are involved. Other topics include leadership of new product development teams, planning and evaluation of innovation initiatives, and management of innovation across organizational boundaries, as happens with alliances or virtual firms.
Planning and Evaluation for Innovation: The Importance of Experimentation and Learning Part Two
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From the course by University of Illinois at Urbana-Champaign
Strategic Innovation: Managing Innovation Initiatives
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University of Illinois at Urbana-Champaign
93 ratings
Course 2 of 7 in the Specialization Innovation: From Creativity to Entrepreneurship
From the lesson
Module 4: Planning & Evaluation & Innovation Across Organizations
Meet the Instructors
E. Geoffrey LoveAssociate Professor
Department of Business Administration, Gies College of Business
Welcome back. This video continues our discussion of
planning and evaluation in the context of challenging innovation initiatives.
Now, this second part is going to be about evaluation.
It's going to complement the discussion we had in the first lesson about planning.
The two are actually going to be pretty tightly linked.
But just to get us located,
evaluation is central to what we think of as the control function of management.
Control involves gathering and interpreting data,
comparing it to what we planned and making course corrections.
Now, we can also see evaluation of new data points as attempts to seek out the truth.
The truth about your technology and your market and what's going on there.
That is if you think back,
these data points are a chance to confirm or to revise
the cause effect model that was described in the discussion of planning.
Now, the reason I said to seek out the truth is that
organizations are biased in their evaluation of data quite often.
This isn't unique to when they're innovating but it becomes particularly
important that there's a particular bias that attains considerable importance.
So, we need to understand what they do wrong and how we can combat it,
how we can avoid falling into that trap.
So, let's look at an example of what I'm talking about.
Imagine we have an outcome where we don't reach
our predicted goal in an area where uncertainty is high.
That is one of our critical unknowns,
one of the areas that we're trying hard to learn about,
sales let's say, are less than expected,
and we're not sure why.
Production is having trouble bringing costs
down because new equipment is not working as expected.
There are many situations we can imagine,
but the key thing to start with is we've fallen short.
Now, we've talked already about having
a rigorous conversation about data such as the shortfall.
And the conversation is likely to be complex.
But at the core, the explanations are likely to fall into one of two categories.
Either the outcome was too low because we didn't implement well.
Okay? It's our problem.
Prediction was okay, we did something wrong.
Or the alternative, the prediction was actually too high.
It didn't match reality.
Our assumptions about this unknown were wrong.
Now you can see, I think being able to look dispassionately at
the data we receive and evaluate it against these categories,
it's obviously critical to learning,
because there's direct implications for action.
If we didn't implement, well,
we just have to work harder.
But if the prediction was wrong,
we need to bring the prediction into line with reality.
We didn't necessarily do anything wrong, we learned something.
Now the trouble is, that a lot of research including
the Dartmouth research among others has found that
companies are going to be much more likely to conclude that
the shortfall was the result of their own poor execution.
Now we'll talk about why,
but let's just emphasize how devastating this is for learning.
This makes it much harder for the company to learn and
adjust to what's actually happening out in the marketplace,
down on the factory floor. The bias becomes-.
We're just going to work harder but in the same direction.
Now, Govindarajan and Trimble,
they actually state that this bias is
the most omnipresent and dangerous enemy of innovation.
That's pretty strong stuff given everything else we've seen, right?
But there's good reason here.
In innovation initiatives, like we've already talked about,
it's rare to get all the predictions right the first time.
And very often, instead of bringing outcomes in line with predictions,
neither were working harder, we need
the predictions to be brought in line with the outcomes.
We are trying to get better at predictions,
trying to figure out these critical unknowns,
trying to establish an understanding of what
the market and technology actually have to offer our initiative.
And the reason this is such a danger is when surprises are over
attributed to our own failed execution rather than the chance to get closer to the truth,
we can't do those things.
So, where does this bias come from?
Well, there's a number of reasons that one could point to.
Some of them are pretty fundamental and psychological origins something called the
threat rigidity response where when people are under threat they tend
to go ahead and focus on execution mode rather than a learning mode.
But for us the one that's most important is going to
arise from how organizations are typically managed in their established operations.
Okay so again we're having
the main stream we're having the mainstream's influence come into the innovation.
So the idea that shortfall should be attributed
to deficiencies in leadership or execution is
deeply rooted in established operations and in fact there
it's it's often functional it's often functional in that performance engine.
You know this is the idea of a performance culture right
where managers are judged on their performance relative outcomes.
Did they deliver what was promised.
This is the idea that revising forecasts down is akin to defeat.
These things are going to be pretty familiar
to anyone who's worked in management for any time.
And this approach can be functional when you can make reliable predictions
because that predicted target that target that Bogey
you have to hit it keeps everybody on focus and motivated.
So perhaps the most prominent example is General Electric which is
famous for its culture of meeting your commitments
and that company's tremendous success over
the past several decades has been a significant part attributed to this idea.
But since the firm's norms and mindset
are overwhelmingly set in its established operations I mean remember we talked about
that back in the discussion of ambidexterity this bias towards
attributing shortfalls to deficiencies in
the organization rather than something different than what we expect.
The environment is deeply embedded in how managers think.
So, what's the solution? Well, the first step
towards the solution is to recognize that you need to offer an alternative system here,
an alternative way of going at this.
Executives are going to be deeply uncomfortable once they take this in,
recognize this bias, because
it appears to undercut their ability to hold people accountable.
When executives see this,
research finds they react in different ways.
One which is fairly common and which is not particularly
effective is to essentially endorse throwing away the plan.
Agreeing that the plan really wasn't consistent with
reality and thus assuming that innovation really can't be managed,
and it's hard to hold people accountable.
This undercuts your ability to build a rigorous learning process into the initiative.
So this is not the direction you want to go.
Another approach that's a little bit better is to go ahead and not to
push for aggressive plans in the way that you would in your established operations.
That way, people are generally optimistic about innovation initiatives
anyway and revisions downward are likely to be infrequent.
So, this bias you're trying to cut it off at the pass.
But this is a very incremental solution.
What's really lacking is an integrated approach that gets at the core of the problem,
like we discussed in the section on planning.
So, a more integrated approach here,
it's going to be built around a one core idea
and that's really going to be the thing I want you to
most walk away from and walk away from this lesson with.
And that is that you can hold people accountable in
innovation initiatives but you hold them accountable for learning.
The question becomes, did you follow a rigorous learning process?
Did you implement that task well?
And we talked about that in the section on
planning and I'll say a bit more here about what it is,
but the key is that this simple change actually has a fundamental effect.
It puts the focus on learning.
It moves it away from outcomes which is exactly what is needed.
Yet it still allows for accountability.
An executive can still work through a rigorous review of the innovation initiative.
In places where there aren't critical unknowns,
where we're relatively well informed,
we can go ahead and act as normal.
But in those critical areas where we do not know what the markets like,
whether the technology is going to work,
the executive can ask to measure progress by saying,
have we become more confident in our predictions.?
Have we done that? Have we reduced uncertainty so that we
have moved from wild guesses about what
things might be towards reliable predictions or at least reasonable estimates?
So, to develop this approach a little bit further,
accountability now is going to be based on questions like the following:
Did the leader take planning seriously rather
than disengaging from the plan to keep that plan as your baseline?
And did you maintain as the innovation leader and update a cause-effect model,
the kind we talked about in planning,
through an evidence based process with a learning orientation?
Did predictions get better,
especially for critical unknowns?
Did the leader make sure the team understood these things,
understood the model and the critical unknowns,
reacted quickly to new information?
Did the team maintain a learning mindset?
So, essentially the accountability around a rigorous learning process,
means that you can build a system of
valuation that fits the needs of the innovation initiative,
addresses that bias and yet is something that executives could become
comfortable with as a way of maintaining accountability in these important areas.
But I think you can see this is not going to be easy.
It's going to be fragile at first and it's going to take constant work.
So, now I can wrap up a little bit or
bring together the previous video and this one and say,
look, we've identified a problem where planning and evaluation of challenging
innovation initiatives are concerned and we've also sketched the outlines of a solution.
The central insight regarding the problem is that planning and
evaluation for challenging innovation initiatives involves learning.
It involves dealing with critical unknowns and so,
it has to be constructed quite differently than
in established operations where the predominant focus is on
delivering outcomes that the targets for them are based on reliable predictions,
the performance culture, and so on.
And I really want to emphasize,
this is the problem you have to reckon with.
Because planning and evaluation are central functions of management.
If you are proactive,
the regular systems are going to take over by default
whether or not you're in a separate unit.
Like in the ambidexterity scenario.
And that'll stifle your attempts to learn
from your innovation initiative and the experiments you are conducting within it.
Now, the insight behind the solution is that you build
that experimentation into the planning and evaluation system.
Essentially, you're planning in experiments around a model of reality,
a cause-effect model of reality where you've identified critical unknowns,
and you evaluate the results with a learning rather than the results orientation.
You take a rigorous approach all the way through.
That's the core of the solution.
And think about this,
you might have heard this phrase,
"innovation can't be managed."
This is quite common, I think.
And there's probably a lot of reasons that people say it.
But I think the core motivation is often this idea that you can't manage the outcomes.
You can't plan, you can't evaluate based on outcomes.
And the trouble with this statement is that it heads you
outside of a planning system and an evaluation system.
It essentially removes those two critical functions of management from the discussion.
And what you've seen here is the outline of an emerging better way.
This is something that the research is just really starting to grab a hold of.
Where the ideas that planning and evaluation systems
need to be adjusted rather than ignored.
You augment them and customize them for innovation.
So, I think this is an important area
for those of you who are involved in innovation initiatives.
And I've actually included some books that in
different ways get this idea among other things at the end of the lesson.
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