Principles of Innovation Management - Faculty &...

________________________________________________________________________________________________________________ Professors Andrew King and Karim Lakhani prepared this note. HBS notes are developed solely as the basis for class discussion. Notes are not intended to serve as endorsements, sources of primary data, or illustrations of effective or ineffective management. Copyright © 2008 President and Fellows of Harvard College. To order copies or request permission to reproduce materials, call 1-800-545-7685, write Harvard Business School Publishing, Boston, MA 02163, or go to http://www.hbsp.harvard.edu. No part of this publication may be reproduced, stored in a retrieval system, used in a spreadsheet, or transmitted in any form or by any means—electronic, mechanical, photocopying, recording, or otherwise—without the permission of Harvard Business School.

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MODULE NOTE FOR INSTRUCTORS

Principles of Innovation Management

Introduction

The art and science of innovation management has changed dramatically in recent years. For much of the

20th century, innovation was thought to occur predominantly inside the walls of large corporations and

under the supervision of powerful managers. In the last few years, however, more decentralized

approaches have received popular attention. Terms such as collaborative development, open innovation,

and open source development have all entered the common managerial lexicon, but the connection

between these different approaches and to older models of innovation management remains unclear. In

this module, we use concepts from operations research and economics to synthesize some of the

fundamental principles underlying both traditional and contemporary approaches to innovation

management.

This note outlines the structure and content of a six-session course module that is designed to introduce

students to the fundamental principles of innovation management. The module is part of a first year

required course in Technology and Operations Management at the Harvard Business School, and it

highlights some key issues faced by managers responsible for innovation and product development. The

module attempts to provide a unifying perspective on innovation management that includes traditional

firm-centric innovation and the newly emerging ―open innovation‖ model.

The module is organized around three inter-connected management challenges:

1. Managing the Generation and Selection of Innovations;

2. Managing the Locus of Innovation;

3. Selecting the Institutional Form Which Will Govern the Innovation Function.

Draft Innovation Management Module Note

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In the first section, we emphasize the importance of systems for generating and selecting innovative

solutions to business problems1. We argue that these two activities are fundamental to any innovation

process. We use this framework to discuss tradeoffs in innovation management and to explain the

metaphor of the ―product development funnel‖. In the second section, we consider how the distribution of

capabilities for generating and selecting solutions influences the locus of innovation and the value of

centralized versus distributed innovation. We provide a simple map for connecting exogenous

conditions to possible innovation sourcing strategies. In the third section, we consider when different

institutional forms best govern innovation management. We use transaction-cost analysis to propose

when innovation is likely to be supported by a market, firm, cooperative, community, or broker.

Module Objectives

This module serves the following purposes:

1. It provides simple heuristics for evaluating tradeoffs in the generation and selection of innovations.

Effectiveness at innovation requires the generation of many ideas and the eventual selection of a

few that are to be implemented. This module proposes that a rational logic of generation can be

developed and applied to most innovation contexts. Instead of relying on idiosyncratic selection

rules, cost and benefits of selection and evaluation need to be explicitly considered so that

appropriate decisions can be made. Finally, the ―product development funnel‖ is used to show

the relationship between alternative modes of generation and selection during the innovation

process.

2. It provides a way for analyzing when innovation should be performed internally by an organization and

when it should be externally outsourced. Many managers implicitly assume that the locus of

innovation is internal to the organization. However this module proposes that this decision

needs to be considered in light of the distribution of problem-solving and use knowledge.

3. It provides a primer on the benefits and disadvantages of different types of institutions as the governing

framework for innovation. The module emphasizes the need to consider how to contract for

innovations and the need for coordination of multiple activities in an innovation project. The

1 Just as the practices of innovation management are changing, so are the accepted terms for its actors and actions.

We use the term ―innovation management‖ throughout this module rather than ―product development‖ to

emphasize that we are considering decision-making about the innovation process and not project management of

product completion, marketing, and manufacturing. We refer to novel solutions to business problems as

―innovations‖ regardless of whether these innovations are superior to present alternatives. The process of creating

these novel solutions we term ―generation‖, and label as ―selection‖ the process of choosing a best alternative among

a range of options. We term the process of forming the alternatives into a useful form as ―implementation‖.

We refer to individuals who are engaging in innovation work as ―problem-solvers‖ rather than ―innovators, because

we wish to reference the ex-ante creativity underlying innovations rather than an ex-post outcome. We refer to

individuals who are overseeing the innovation process – particularly the generation and selection of novel

alternatives -- as ―managers‖, and we refer to structures of ownership and control as ―institutions‖.


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interaction between contracting and coordination then drives the most appropriate institutional

form for innovation.

4. Explanations for why the location and institutional framework for innovation is changing. This module

provides explanations for the recent trend towards more distributed innovation. The distributed

nature of the innovation process is discussed in both historic terms and through the consideration

of the user-innovation paradigm. Increasing distribution of problem solving knowledge and use

knowledge are considered as drivers of a trend towards more distributed innovation processes.

Module Structure

The module comprises five case studies and one concluding lecture. Two pairs of cases are used to

facilitate discussion of the module concepts. Each case is intended to reinforce the previous material and

introduce new concepts. The case studies include:

Generation and Selection of Innovations

IDEO Product Development

Team New Zealand

Locus of Innovation

Threadless: The Business of Community

InnoCentive

Institutional Forms

Radical Collaboration at IBM

Open Source: Salvation or Suicide?

This module note is organized by section according to the themes outlined above.


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Generation and Selection of Innovations

“Enlightened trial and error succeeds over the planning of lone genius” – IDEO motto

Innovation is at the heart of new product and service development. It can be found in the creation of new

elements, new applications, or new combinations of existing components. Innovation is necessarily a

journey into an unknown world. If innovations could be determined analytically, new products and

services could be dialed up and generated automatically. But such easy automation would also mean

that the created innovations alone would provide no business advantage. Valuable innovations are by

definition novel and non-obvious. Hence the innovation process is fraught with uncertainty and requires

a search which is both murky and undefined. Thomas Edison’s discovery of the best filament for the

electric light bulb illustrates the search process through a solution space that many problem solvers

undertake to create their innovation. He is noted to have said: ―Before I got through, I tested no fewer

than 6,000 vegetable growths, and ransacked the world for the most suitable filament material.i

Scholars have conceptualized the search for an innovative solution as akin to searching for a mountain

peak in a rough landscape with numerous peaks and valleys. On the horizontal axes, forming the surface

of the landscape, are the various input elements that might comprise a good solution. On the vertical

axis, creating the elevation of the surface, is the performance of the design relative to some goal (or

objective function). Imagine for example, that a problem solver is looking for the best mixture of

materials to use as the casing for a cell phone (see Figure 1). Depending on the mixture of two elements,

the case will differ it its performance (e.g. it will resist scratches, cracking, look attractive, etc.). The

problem solver’s mission is to find a good mixture of the components.

The success of the problem solver’s search for a good solution is a function of both knowledge and

chance. The rougher the landscape and the scarcer the problem solver’s knowledge of its terrain, the

larger the role played by luck. If the landscape is completely unknown and rough, the problem solver

can do nothing but experiment with different possible solutions and hope for the best (as Edison did). If

the landscape is completely smooth, the problem solver can use his or her knowledge to estimate where

good solutions may lie. Even if the shape of the innovation space is poorly known, they may be able to

use an experimental-learning method to find a good solution.ii If, as is often the case, the landscape is a

combination of roughness and smoothness, both chance and knowledge may play a role.

Figure 1 shows a surface which is generally rough, but locally smooth. Problem solvers would face such

a landscape when they cannot estimate how large changes in attributes will influence performance, but

can estimate how small changes in the design will affect performance. For example, the problem solver

may not be able to determine the best type of tail configuration for an aircraft, but once this configuration

has been chosen, they can quickly converge on a good design. Knowledge of particular regions of the

search space can sometimes bias inventors to continue to look near where they have expertise – rather

than look for a better solution elsewhere. For example, almost all of the teams competing to win the

Kramer Prize for the design of a human-powered airplane chose to put the tail after the wing. Only the


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eventual winner, Paul MacCready decided to try putting the ―tail‖ before the wing (in what is sometimes

called a ―canard‖ configuration). After doing so, he realized that at the very slow speeds of human-

powered flight such a configuration had distinct advantages and it was for this reason that the Wright

Brothers had also chosen the configuration for their own underpowered airplane 80 years earlier.

Decades of design experience with highly powered aircraft had provided extensive knowledge of the

properties of trailing tails and had made all but unthinkable to use a canard design.

Figure 1: A Two Dimensional Rough Landscape

Analyzing the Value of Generation and Selection

Uncertainty about the location of good designs within a search space drives inventors to use

experimentation to search for ―peak‖ solutions within the space. How best can designers manage the

experimentation process? Two issues are often central: how wide should the search be, and how many

experiments should be run before a ―good enough‖ solution is picked? Answering these questions

requires considering how alternatives for testing are generated and how they are selected.

Almost all design processes include a way of generating alternatives and then evaluating them relative to

some goal (or objective function). Operational analysis of this process often begins with a simple model

of the generation process. The simplest process, and one that is often not far from reality, is that the

problem solvers begin by simply guessing what might be a solution. Even if the problem solver seeks

solutions by guessing, the value of each guess can still be estimated. Imagine that a problem solver is

looking for the best material to use for a particular product and that there are an almost infinite number

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of possible alternatives. It is too expensive for him to test them all, but he wants to use his time and

resources efficiently to find the best possible material. Since he knows nothing about which material will

be suitable, he determines what should be the objective function for the material and develops a test

which rates the material relative to this objective. He then starts picking materials to test at random.

After a few tests, our hypothetical problem designer would discover that it was getting harder and

harder to find a better material, and when he does find one, it is only slightly better. The diminishing

returns of this experimentation are cause by a well known statistical process. As the best alternative gets

better, the chance of finding a better one in each successive experiment drops2. However, unless the

space can be searched complete it doesn’t drop to zero -- there is always a better solution out there -- but

find it would be costly that it makes sense to give up at some point and pick a ―good enough‖ solution.

The value of generating additional alternatives

When should our problem solver decide to stop generating new alternatives? This is an example of an

―optimal stopping problem‖, and companies and people face it almost every day. For example, if you are

looking for the best table lamp for your house, you could exhaustively search till you identified the

absolutely best one, or you could decide sometime earlier that one was good enough. Precisely

determining the best time to stop is both complex and dependent on the precise nature of the problem. A

good rule of thumb is that one should stop when the expected return from testing an additional

alternative drops below the cost of evaluating that alternative []. Note, that the expected value of each

additional test is not the same as the realized value of an additional test. Just because the second

lampshade is worse than the first does not suggest that the search should be stopped. Rather, one should

think about how the pattern of returns changes over a series of tests.

What determines the degree to which additional alternatives are likely to provide additional value?

Surprisingly, it turns out that this is one case where variability is a good thing. Imagine for a moment

that all of the table lamps in the world are all pretty good. They are based on the same design and differ

only slightly in color. Clearly, search provides little value. Now instead imagine that all of the table

lamps in the world vary immensely in their design. On average, they are still pretty good, but some of

the designs are awful and others are fantastic. Clearly, evaluating a number of alternatives before

making a choice could be very beneficial.

The pattern of how value varies across the solution space (i.e. the shape of the density function) can also

matter []. Some solution spaces have a limited upside, while others have limited downside. Imagine, for

example, an innovation which addresses a relatively small market. Even if the innovation allows the

implementing firm to capture most of the market share, the return may still be bounded. Other

innovation landscapes are likely to yield a few blockbusters. Pharmaceutical companies are example of

an industry where innovations can provide an almost unbounded reward. Not surprisingly, when the

upside is greater, additional experimentation and testing makes sense. For mainstream problems, the

pharmaceutical industry conducts primary testing on hundreds of thousands of chemicals and performs

2 This process can be modeled as the nth order statistic of a distribution.


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secondary testing on tens of thousands. In contrast, for small and limited markets, many fewer chemicals

are tested.iii

Regardless of the exact distribution of the value of different alternatives, a simple relationship exists

between the number of alternatives which should be tested and the costs of the tests.

Eq. 1: N* = b/c

The optimal number (N*) is inversely related to the cost of the tests (c). Second, the number of

alternatives which should be tested is directly related to the scale parameter (b) of the distribution of the

space to be searched3.

Returning to our example of a search for a good table lamp, the equation indicates that the greater the

variance in the quality of lamps, the more alternatives should be inspected before one is selected. If the

cost of search doubles (perhaps it becomes harder to drive to look at lamps) the number of lamps

evaluated should be cut in half.

Strategies for improving alternative generation

Analysis of the statistical processes underlying the generation and search process provide additional

insight into how innovation should be managed. Unlike many settings in operations management, where

variance causes problems, variance in innovation can lead better solutions. Greater variance increases the

probability that a designer will come up with a truly great idea, and if the bad ideas can be thrown away

with little cost, then an increase in variance actually increases the expected value of design. Figure 2

shows the expected value of the best of N draws from two normal distributions with identical mean. As

shown, the expected value of N draws from the distribution with greater variance is always higher.

3 The scale parameter b increases with the variance of the underlying distribution.


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Figure 2: The expected value of the best alternative from N random choices from a normal

distribution. In this example, a random number was drawn from a normal distribution. If the new

number was higher (better) than those drawn previously, it became the current leader. Note that

with each successive draw, we expect that the value of best alternative will rise (it cannot fall), but

that the expected improvement falls. The nature of the figure is dependent on the underlying

distribution. If the distribution is very tight (the variance is very small), the expected improvement

from each additional draw is much smaller.

What does this mean for managers? It suggests that managers should think actively about how to

increase the variance of the ideas created by their problem solvers. What are some ways they can

accomplish this? The IDEO case, the first in this module then provides a useful backdrop to discuss both

the value of generating many alternatives and the organizational processes that enable such generation.

IDEOs working environment, its processes of hiring, its brainstorming methodology, and its design

management all help insure that for each problem, a wide range of alternatives is tested.

One problem with increasing the variability of the alternatives explored is that it also increases the

variability of the design process. While casting a wider net will produce a better outcome on average,

some searches will come up with wildly fantastic ideas, while others will produce bloopers. For some

applications, time or strategic pressures may increase the value of minimizing the possibility of a poor

quality design. For example, a well entrenched firm may be willing to give up some potential for a

brilliant new product in order to avoid a real klunker. Innovators working with such goals may find it

more valuable to narrow the search space to better known areas.

The cost of evaluating


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A higher evaluation cost reduces the number of design alternatives which can be economically

considered, and diminishes the expected return from innovation. As a result, managers have an incentive

to consider how to reduce such evaluation costs.

The cost of evaluation is made up of several parts. First, there is the obvious cost of the actual test of the

design alternative. For example, (as discussed in case 2 of the module) when teams competing in the

America’s cup want to evaluate a new hull design, they often have to build a 1/4th scale model of the hull

and test it by drawing it through a large tank of water. This testing process can cost tens of thousands of

dollars and take more than a month to complete. The delay caused by testing represents another part of

the cost of evaluation. Delay in choosing a component of a design may impede the development of other

elements. For example, the basic hull design for an America’s cup yacht must be set the team can begin

to conduct finer testing and refinement of other aspects of the boat design. Alternatively, delay may

result in the completion of a project and a reduced competitive position in the market.

In his design of a human powered airplane, Paul MacCready chose materials for his models which he

knew could be repaired quickly and easily. As a result, his team was able to cycle through many move

versions of possible wing and tail configurations. These additional tests meant that they could find a

better solution before time or cost forced them to quit. The speed with which they could test new

configurations and repair them also helped them keep ahead of their competitors for the prize.

Figure 3 shows a heuristic for determining when to stop testing additional alternatives. When the expected

marginal value of an additional test drops below the total cost of an additional evaluation, generation of

additional alternatives is not warranted.

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The accuracy of testing is closely linked to its cost. Inaccurate tests may cause managers to select

suboptimal alternatives – thereby failing to realize some achievable benefits. Alternatively, inaccurate

tests may require repetition to achieve the desired precision, and this obviously will increase the cost of

evaluation. In considering the accuracy of evaluation, four elements should be considered. These

correspond to basic principles of analysis and inference from statistics.

Fidelity is the degree to which the test is a good proxy for the real goal for the design. Almost

every test is an abstraction from the real objective function. If this abstraction is far removed,

then the it is less likely to select the better alternatives.

Identification is the degree to which an assessment can be linked to the alternative being tested.

Suppose for example, you wish to test a new bicycle wheel, but to run your test you have to

change the bike’s frame. Even if you can measure performance precisely, you may not be certain

that you are measuring the effect of the wheel.

Representativeness is the degree to which the results of a test would remain the same in different

contexts. Ford Motor ran into this problem in the testing the tires for their Explorer SUV. Their

analysis suggested that the tires were sufficiently durable, but they then specified a different

pressure than the one used in the test for use with the Explorer. In hot conditions, the tires

tended to fail catastrophically, causing the SUVs to roll over,

Precision almost all tests contain some error. The greater this error, the more tests will be required

to accurately measure the value of an alternative.

Strategies for reducing selection costs:

What are some approaches for reducing the cost of evaluation? Scholars have identified several

strategies.

Test Early. Through both anecdotal and quantitative analysis, researchers have consistently

reported that managers of innovation projects tend to wait too long before testing the merit of

their design. As a result, researchers have suggested that firms build quick prototypes of their

designs and get an early appraisal. This prunes off ineffective designs before they have soaked

up significant investment.

Locate Designers Near Users. One way to encourage early assessment of ideas is to physically

locate designers near users. This helps to insure that designers get rapid feedback about their

ideas. In some cases, users may be incorporated into the design team. Because users often have

unique and private information about design merits and are fully motivated to maximize this use

value, they can help insure that designers get early feedback.

Verify Objectives. Improving the accuracy of assessment can also reduce costs. First, in cases

where the firm is using a model of customer demand as a proxy for real preferences, it is


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absolutely critical that the firm repeatedly validate their model. One of the most common

problems in innovation is that designers create new ideas for solving yesterday’s problems. As

user needs are met, other ones may become more important, and this can shift the criteria for

analyzing new innovations. For example, when storage capacity represented a substantial

bottleneck to computing power, customers preferred disk drives with higher capacity. As a

result, firms could logically orient their innovation efforts to improving such capacity. Once this

bottleneck was surmounted, customers became more interested in other attributes of disk drives

– for example, their durability – and firms which continued to emphasize designs which

maximized capacity suffered.

Use Controlled Experiments. One of the best ways to ensure that the test measures the innovation

under consideration is to run a horserace between two competing designs which differ in only

one way. This helps reduce the impact of all of the other design elements. It also can reduce

some of the sources of measurement noise. For example, when drug companies test the efficacy

of new drugs they administer drugs and placebos to two groups comprised of similar patients.

By using similar patients and running them through an identical routine of drug delivery, they

hope to rule out any other factors which might give a false reading with respect to the drug’s

performance.

The Design Funnel and Gated Processes

The process of generation and selection which we have discussed above is usually repeated within any

given innovation process. For example, after Paul MacCready chose to use a canard for his human-

powered airplane, he then conducted additional analyses to determine the best shape for the canard.

Such shape analysis could only be done after the basic tail configuration had been chosen. Usually, any

design process works through many stages which narrow the remaining problems to be solved.

The ―Design Funnel‖ is a common metaphor for the process of narrowing the scope of investigation.

Most companies use the metaphor to explain their internal gated process. Design alternatives for testing

are generated between the gates and a winnowing process occurs at the gates. After each gate, new

alternatives are again developed, but these are constrained by previous decision and thus are of a more

limited nature. For example, the main theme and elements of a design may be generated and tested in

early stages and components of the design refined in later stages. Over time, the number of alternatives

in consideration becomes smaller and the potential for major innovation decreases. Eventually, activities

in the funnel shift from innovation to implementation. As designs progress through the funnel,

management of the innovation process becomes less important, while more important becomes

management of the implementationiv.

Where should gates be placed in the innovation process? Logic and empirical analysis suggest that they

should be located where irreversible branches occur in the design process. The branches may occur where

decisions are required to allow more refined development. For example, programmers may need to

choose a basic architecture before components can be developed. Such irreversible branches also occur

where fixed investments are needed.


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Placing gates at points in the development process where substantial investment is required makes

intuitive sense, but some scholars have argued that gates may also be needed in the intervening period as

well. Bell and Thomke () argue that designs begin to drift from their desired objectives over time and

thus problems accumulate between gates. Bell and Thomke argue that the rate of this accumulation and

the cost of evaluation can be used to calculate whether one or more intervening gates is needed to get the

development effort back on track.

Figure 4 shows a schematic of a standard product development funnel.

Two case studies within the module, IDEO Product Development and Team New Zealand, provide rich

discussion materials for developing insights into important issues in managing the generation and

selection of innovative solutions to business problems.

Case Study: IDEO Product Development

At the center of the IDEO case is a decision faced by management to accept or reject the development of

the Handspring Visor handheld computer. If they accept the project, they will need to develop a new

design in less than half the time it took IDEO to develop the breakthrough Palm V. The decision creates

conflicting incentives for IDEO. On the one hand, they project could be profitable and help them develop

a new client. On the other, the Handspring project is mostly incremental in nature and thus does not

draw on their skills in innovation.

The case serves as a backdrop to understand how a firm specializes in creating innovations on demand

by having a system that generates many alternatives and then goes through a process of selecting

between various alternatives.

IDEO’s process for generation of innovations is dependent upon several elements. Each of these

elements helps insure that IDEO considers a wide number and variety of ideas. That is, they sample

alternatives from a large wide distribution of possible solutions.


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Diverse People: IDEO hires individuals with very diverse backgrounds and abilities. Not only are

there people with industrial design backgrounds, but also engineers, physicians, ethnographers,

artists, psychologists and even MBAs. Management at IDEO believes that having staff with a

diversity of backgrounds maximizes the range of alternatives that will be considered. All teams

are staffed with cross-functional individual to ensure this maximum diversity in ideas.

Structured Brainstorming: IDEO believes the best way to utilize the diversity of its staff is to have

strict rules on brainstorming sessions so that the maximum number of ideas are generated

initially. Some of the rules for the structured brainstorming include: encourage wild ideas; defer

judgment to avoid interrupting the flow of ideas; build on the ideas of others; go for quantity (150

ideas in 30 to 45 minutes); visualize and sketch ideas. The culture of brainstorming at IDEO is

such that often individuals from outside the focal team are invited to participate so that diversity

and idea generation is further maximized. IDEO has in fact trademarked their brainstorming

session as the ―Deep Dive.‖

Culture: The culture of IDEO is one around encouraging failure. IDEO management has realized

that key to innovation is many trials and attempts which are often failures but critical for learning

and discovering what may be the right solution. Similar to Edison’s search for a filament, IDEO

encourages a culture that celebrates failing often so that success comes earlier. Failure is not seen

in a negative light but actually is seen as necessary for success. This is coupled with an emphasis

on sharing and respecting the ideas and work of the diverse team members. IDEO’s culture then

ensures that individuals feel comfortable imagining and raising as many ideas as possible

because they know that they will not be judged harshly for ―crazy‖ ideas that may not seem to

work initially but may spark the ultimate solution.

Knowledge Management: IDEO has created a very unique knowledge management system that

relies on the accumulation of various artifacts from prior projects which enables future idea

generation. As project teams explore various technologies, materials and use a variety of props

for inspiration in their design work, the best of these are nominated to be come part of the firm’s

―Tech Box.‖ This repository is then used by future projects to search for new ideas and to be

inspired by curiosities and gadgets that may have helped other teams in the past. The Tech Box

serves to gather useful knowledge from projects in the past and make them available for future

projects so that they themselves can use the artifacts to generate many more ideas.

While idea generation is paramount at IDEO, the simultaneous whittling down of ideas and creation of

the final design via a process of rejecting a majority of the ideas is accomplished by two mechanisms:

Rapid Prototyping: Ideas generated through brainstorming are quickly made ―real‖ through a

fanatical belief in the power of prototyping. Prototypes at IDEO are : ―Rough, Rapid and Right.‖

IDEO teams work with designers and engineers to transform ideas into reality so that the diverse

team of individuals can quickly assess if the ideas are viable and interesting. Similar to

brainstorming, most prototypes are going to fail, however the belief is that by continuing to


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prototype, the ultimate design will emerge. Hence prototyping plays a critical role in both

selecting the next design focus but also providing feedback to the design team as to areas that are

most fruitful for further idea generation.

Management Control: Project leaders and managers play a critical role in the selection process.

They don’t claim to have the right answers, instead they focus in on the process. During the

brainstorming stages, they act as the guardians of the Deep Dive and the structured

brainstorming – by ensuring that all voices are heard and that the maximum number of ideas are

generated. However, once the brainstorming phase is over, managers assume a more controlling

style (―the adults take over‖), where they push the team to follow through on some of the best

ideas so that they can be transformed into prototypes and eventually one or two final concept

products.

It is important to realize that IDEO still follows the funnel concept outlined above. Their skill and talent

lies in the recognition of both the importance of generating many ideas and having a process that can

reduce the ideas to a manageable number so that in the end an innovative product or solution is

developed at the end of the funnel.

Case Study: Team New Zealand

The Team New Zealand case puts the students in the midpoint of a yacht design program for the 1995

America’s Cup competition. The team has four months and a budget of $20 million to build two boats.

The question faced by team is if they should build two boats of identical hull design right now, build two

boats of different hull designs now, or build one boat now and wait for results after additional testing to

build the next boat.

The Team New Zealand case allows exploration of two important elements of managing the generation

and selection of design alternatives. First, it shows how human knowledge can be used to narrow the

scope of experimentation. Second, it allows students to evaluate a stopping rule in deciding whether or

not to continue with one stage of design and experimentation or move on to the next. Third, it allows

experience with how testing can be used to narrow the search space.

Design Space Exploration: As figure 1 has illustrates, problem solvers in general and yacht

designers in our particular setting, place themselves on a rugged design landscape as they search

for the most optimal solution. The case indicate that Peterson is an expert designer and

understands the waters of San Diego enough to have already chosen a general hull design. This

general design narrows the space in which TNZ can search for a best design. Experiments on

multiple models allow the team to further narrow the field of rival designs.

Stopping Rule: The team is faced with the decision of whether to continue tank and tunnel testing

of hull models or choose a hull design and move on to the design of the keel. The case provides

enough facts that students can calculate whether the improvement which can be obtained from

delay surpasses the improvement which may result from keel improvements.


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Experimentation Strategy: The case also allows consideration of different keel alternative testing

strategies. The team chose to build two identical boats to allow controlled experiments of the

keel – and thereby reduce the cost of testing. Two boats allowed the team to change one element

at a time in the keel design and then directly observe the difference in performance between the

control boat and the ―changed‖ boat.

Team New Zealand realized that the final changes in the design of the keel and the boat would be

driven by the crew. Team New Zealand management believed that in the end that it’s the crew

that has to sail the boat and if their feedback is incorporated directly into the design process then

the overall performance of the boat should also increase. The co-location of the crew with the

designers and their rapid prototyping tools enabled them to quickly provide feedback on the boat

performance and the changes required to boost speed. Designers would get a variety of feedback

from the users which could be simulated and then prototyped if necessary.

In total, Team New Zealand’s experimentation plan reveals the value of testing which allows

high fidelity, identification, representativeness and precision.

Team New Zeeland was successful in its endeavor to capture for the first time the America’s Cup

in 1996. The crew through many months of controlled experiments amongst the same two boats

really understood the intricacies of the boat and the designers had fine tuned the vessel design

based on limiting their search for major alternatives and instead focused on smaller, more

incremental changes, that accumulated to large advantage. Thus in the middle of the funnel,

given an adequate design that meets certain minimum criteria, attention needs to shift to

refinement and incremental improvement so that a finished product can be delivered.


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Managing the Locus of Innovation

―No matter who you are – most of the smartest people work for someone else.‖ -- Bill Joy, Sun Microsystems

In the section above, we assumed that a small group of predetermined people carry out all parts of the

innovation process. Selected designers (e.g. a design group within IDEO or the TNZ team) generate the

alternatives to consider and determine the objective function against which these alternatives should be

judged. They also evaluate and select the alternatives which will be pursued. In this section, we begin to

explore when and why generation and selection might occur in a more distributed manner.

The use of distributed systems for innovation has received great attention of late. Numerous authors and

consultants have espoused the advantages of ―open innovation‖, ―user innovation‖, and ―open source

development‖. The general claim is that such systems are unprecedented, but in fact most have long

histories. Open innovation – the outsourcing of innovation to service providers – has antecedents in 19th

century subcontracting of building architecture design elements. User innovation – the invention of new

products by the users of related products – has many famous precedents (including George Eastman

(inventor of a better camera) and Henry David Thoreau (inventor of a better pencil). Open source

innovation has antecedents in the open proceedings of scientific communities. Yet despite such

precedents, the degree to which these structures are being use is novel, and that has encouraged

additional scholarly research and additional need for pedagogy.

Distributed innovation often involves a change in the nature of the relationship between the solution

―seeker‖ and the problem ―solver‖. When a seeker can determine the best solver, they can hire them for

their services. For example, a company may be able to determine that a company like IDEO has unique

design knowledge and systems to deliver a quality solution. They can then hire them to deliver the best

solution they can find within some budget. Increasingly, however, seekers face greater difficulties in

determining who could be a good solver. Globalization has increased the number and quality of

designers. The rate of change in technology has increased the value of young designers, making them

both more numerous and harder to evaluate. As a result, for some activities like computer programming

and numerical analysis, the number of potential solvers can range from the thousands to the millions. In

addition, quality differences among these solvers can be dramatic. While an average computer

programmer writes X lines of code a day, top programmers can write Y lines. Quality is also difficult to

judge. Consider again the example of computer programming: many top codes are young and lack a

proven track record. Many of the systems and programming languages they use are only a few years old,

and this makes it difficult for managers to judge the quality of programmers.

Thus, firms increasingly find themselves with problems for which there are many potential solvers of

unknown quality. The answer depends on the degree to which the seeker can determine the locus of the

design and use knowledge or skill needed to generate and select a good solution. Figure 5 provides

examples of how the use of distributed innovation may depend on the degree to which solution or use

knowledge is known.


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Beginning with the vertical axis, the locus of solution knowledge is unknown when there are many

potential designers of varying quality. If there are only a few possible designers or their quality varies

little, then the location of design knowledge is more easily known. When the locus of design and use

knowledge is known, seekers can more easily hire designers to create a solution. When the locus of

design capabilities is unknown, seekers can benefit from broadcasting their needs and then evaluating the

solutions they generate.

Use knowledge, like solution knowledge, is also critical to successful innovation. As discussed earlier,

innovation requires both the generation and selection of possible solutions. Accurate selection depends

on knowledge of the way an innovation will be used. For example, users of sporting equipment like

snowboards, windsurfers, or kite sailors often develop particular tricks which require the equipment to

operate in unexpected ways. Windsurfers, for example, will sometimes flip their boards on the side and

ride the edge (or rail) rather than the bottom. Knowledge of these uses is critical to selecting the best

designs for new boards. If the location of such knowledge is known, seekers can hire users to help them

select better designs. If such knowledge is unknown, seekers may benefit from broadcasting several

design alternatives to users to allow them to comment or select the better designs.

Accessing seeker knowledge and preferences requires consideration of what information is designed

from users. In some cases, the preferences of an average user is desired. In other cases, the preferences of

lead users – those users with preferences which anticipate those of future users -- are more useful to

seekers. Regardless, the degree to which the seeker can determine these users influences the value of

different approaches to selection. If the appropriate and representative user can be determined ex-ante,

then the selection process can be done by a small, closely controlled team. If not, a voting system will be

more valuable.

As shown in Figure 5, when knowledgeable designers and users can be determined before an innovation

project begins, they can be brought together to form a single development team. The close connection of

designers and users allow rapid feedback and improvement. Because design and use skill is known, few

design or use skills are excluded by this integrated process. Such integrated efforts are common in

manufacturing improvement projects where the set of potential users is both small and specified and the

number of capable designers is limited. Many companies will staff development efforts with designers

from an R&D center and users from the eventual manufacturing division.

When the locus design capabilities is known but the locus of user knowledge is not (quadrant 2), more

permeable innovation systems tend to predominate. Firms like Zara have developed sales systems to

allow customers to select the designs. While other fashion companies use tightly controlled teams of

designers and fashion experts to determine each new line of clothes, Zara has chosen to create small

batches of numerous potentially popular designs and then use actual customers to select what is the

trend. Other firms explicitly use even more democratic methods for allowing customers to select among

alternative designs. The Cooper-Hewitt National Design Museum has created an interesting twist on

their National Design Awards, called the People’s Design Award. It lets anyone nominate and vote on a

design they like.

http://peoplesdesignaward.cooperhewitt.org/2007/


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When the locus of design capabilities is unknown but use knowledge is known (quadrant 3), design

tournaments are more common. Tournaments essentially represent a kind of reverse auction. A prize is

posted for a service, and designers bid with possible solutions. The greater the number of potential

designers and the more variable their skill, the more appropriate is a tournament structure for innovation

management.

When design or selection is done by agents outside of the firm, managers must think carefully about how

to create an innovation process which encourages the ―right‖ designers and users to participate. Consider

first the case of designers, it is usually in the seeker’s best interest to develop a competition wich insure

that the best designers participate. To do this, should he or she encourage or discourage participation

(for example by raising or lowering a fee to participate)? Research suggests that the answer depends on

the degree to which the innovation process involves a well known solution space. If the solution space is

well known and smooth so that creating a good design is largely a function of planned effort, then large

design tournaments may be worse that smaller ones. The numerous other entrants tends to discourage

any single entrant from exerting much effort – so much so that the loss of effort outweighs the value of

additional contestants. In contrast, if the solution space is rough and thus must be explored through

experimentation, having a greater number of participants is always beneficial. In this case, any losses in

motivation caused by a greater number of participants is more than compensated by the increase in the

number of participants (and thus a higher number and variety of experiments).

The cost of evaluation also may influence the usefulness of a design tournament. The higher the cost

evaluation, the less beneficial is a tournament. This is because with higher costs, the stopping point for

evaluating the innovations is lower. If the cost is high enough, the firm might actually want to create a

screen which causes only the best designers to participate in the competition. Screens (such as entry fees

or conditions) may be designed to allow better designers to self-select into the competition while keeping

out th less knowledgeable designers.

Case Examples

In the TOM RC course, we use the Innocentive case to explore issues with innovation systems in

quadrant 3. Innocentive acts as an intermediary between firms seeking a solution to a particular problem

and tens of thousands of designers. The firm contracts with Innocentive to broadcast the problem to their

designers and start a design tournament. After a series of selection stages, the contracting firm eventually

chooses one or more winner who receives the prize. The tournament structure makes sense for the

seekers who work with Innocentive because they have the knowledge to be able to assess the quality of

possible solutions, but they do not know where solution knowledge may lie. For example, Innocentive

has been conducting a tournament for a non-profit who is seeking a ―bio-marker‖ for the neurological

disease ALS (Lou Gehrig’s Disease). This bio-marker would allow doctors to determine who is at risk for

the disease and to measure its progress. In the first round of the competition, the best suggestion came

from a dermatologist who had noticed a connection between some skin blemishes and ALS. What is

remarkable about this (an other examples at Innocentive) is that the best idea came from a completely

unexpected source. It came from a part of the solution space that the seeker would never have

considered exploring.


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For many applications, a single seeker may have difficulty identifying the location of knowledge needed

to select the best design. This the 4th quadrant in Figure 5. Why might it be difficult for a single company

to ascertain the demands of potential customers? Several possible reasons exist. For example, customers

may have private information about local conditions which they may not be willing to share with the

designing company. Alternatively, the exact nature of the customer’s objective function may be difficult

to transfer to the designer – perhaps because the customer himself can’t explain it or it is too complicated

to transfer easily. Finally, there may be so much intra-user variance or intra-temporal variance that it is

costly to create an accurate model of customer demand.

This last problem is evident in the Threadless case which is used in the TOM Innovation Module. In the

Threadless case, T-shirts are designed by a distributed community of users who then vote on their

favorite designs. Fashion, including T-shirt fashion, is notoriously fickle and variable. What is trendy on

one day may be completely passé on the next. What is cool among one group may be completely gauche

in another. Even large and experienced fashion houses have difficulty evaluating and responding to new

fashion trends.

Threadless determines the designs to print on its T-shorts by having the user community vote on which

one of hundreds of designs is best. Designers compete to be selected in this group – submitting almost

800 T-shirt designs a week. The community helps guide these designers as they develop their designs

for submission. Thus, many designs are not submitted for evaluation until a subset of the community

suggests they are ready. Workers at Threadless do none of the design and only some of the final

selection. In this fully outsourced model, it provides the platform on which design will be performed and

prints up the T-shirts for waiting customers.


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Figure 5 shows a map of how the geography of knowledge influences the structure of product development

efforts.

Quadrant four also includes other types of innovation systems with far more economic import than

Threadless. Open source programming efforts like Linux and MySQL also fall into quadrant four. Open

source development has become enormously important. For example, the common LAMP architecture

that underlies the majority of web sites is all comprised of open source code (Linux, Apache, MySQL,

PHP, Pearl, or Python).

Where Threadless is a ―design first‖ approach in which artists put forward their work for the emotional

reward and credibility which its selection will bring, most scholars believe that open source development

systems emphasize a ―need first‖ approach. Users of the open source code uncover a local need

(problem) and then seek a solution. In many cases, they initiate the development of the solution

themselves. In other cases, they propose the need to a community of programmers which develops the

solution. As in Threadless, the designs are often reviewed and modified by a small group of

programmers before they are submitted for inclusion in the final code base. Solutions (new pieces) of

code are adopted into the general codebase based on the opinion of the community and its leaders. While

this process of selection is done by a more concentrated group than is the case in Threadless, the process

of evaluating the code for potential problems is extremely decentralized. Essentially every user of the

system is engaged in testing the code base.


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Constraints on the Use of Distributed Design or Selection

Given the success of distributed innovation and selection systems, why isn’t all innovation conducted in

this way? Part of the answer is that the ideas of distributed innovation are still diffusing. Another part

has already been alluded to earlier in this document. In distributed innovation or selection, the designer

(or the selecting user) is no longer under the control of the firm’s managers. They are acting in their own

interest. This can be an important advantage of such systems since such incentives can motivate efficient

investment in design or evaluation. These incentives can create problems, however, when there is a

misalignment between the objectives of the firm and those of designers or users.

It might seems that a seeker’s objectives would perfectly correspond with the eventual users’ objectives,

but this is not always the case. Production costs and strategic considerations often mean that the best

choice for users is not the best choice for the seeking firm. As a result, many firms that allow users to

directly participate in the selection of innovation retain some control that allows them to filter out designs

that are not suitable. For example, at the Threadless T-shirt company, the community narrows the

weekly designs from about 800 to 100. Threadless personnel then select 7 to print from the remaining

100. By conducting the final cut themselves, Threadless prevents production of t-shirts which might

damage their brand.

Outside innovators are even more likely to have diverging objectives. Most critical are two problems

related to the difficulty of contracting on ideas and the difficulty of coordinating among multiple design

efforts.

The Disclosure Problem

For designers to be willing to participate in design tournaments, they must believe they will be able to get

some value for their idea. Revealing the idea publically may reduce whether and to what extend a

designer can appropriate value from his effort. Some ideas can be protected by legal property rights

(patents and copyrights), but many cannot. For the majority of designs, secrecy provides the best

mechanism for appropriating valuev.

When proposed design solutions are revealed as part of a tournament, participants may fear that their

ideas will be expropriated by others and thereby choose not to provide designs. As shown in Figure 6,

such appropriation has occurred for the designs on the Threadless site.


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Figure 6. The Threadless T-shirt on the left was copied by another designer (right). Because only the

expression is protected by a copyright, the idea can be freely modified and reproduced.

Just as designers may fear to reveal their ideas in a tournament, so too companies may fear revealing their

need. The identification and structuring of an important problem can often be as important an invention

as the solution itself. Revealing a demand for a solution to a particular problem may also provide

competitors with useful information about development plans.

The Coordination Problem

The examples of open innovation considered so far in this module note (Threadless and Innocentive)

involve design efforts which were independent from each other. The value of one T-shirt on Threadless

does not depend on the work of another designer. The value of one Innocentive challenge does not

depend on the success of another. For many products, such independence is difficult to achieve. For

example, the design of a braking system for an automobile depends on other aspects of the cars design

(its suspension, electronic controls, etc.). To make the entire car operate effectively, the designs need to

be coordinated.

Two approaches are common for addressing interdependency across designs. The first is to try to

partition the tasks so that each design is as independent as possible. For example, the designers of the

brakes for the automobile might also design the rest of the wheel or part of the suspension. The second is

to try to specify how each design will operate. For example, such an interface document might specify

exactly how the breaks would attach to the vehicle, how they would be controlled, the foot-lbs of

breaking force they would apply, and so on. In essence, this latter approach attempts to add the interface

into the design contract, while in the former approach, managers try to find a way to cut the design work

to minimize the need for such contracts.

Governance of Innovation

“In the future, there aren’t going to be any managers of innovation” – Eric von Hippel


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In the above section, we identified several places where managers might exert influence over the design

task. It might seem unimaginable that design might be accomplished without these tasks, but a few

vanguard scholars are extrapolating from recent trends to predict precisely that will happen. In this

section, we consider how and why innovation is governed in different ways, and how this might change

in the future.

Introduction to the Theory of the Firm

Innovation, like any economic activity, can be organized by the ―invisible hand‖ of the market or the

―visible hand‖ of management. When a company like Threadless or Innocentive initiates a design

competition, they are using a market mechanism (a form of a reverse auction) to source a new innovation.

When workers in a company like IDEO engage in a design task, they do so under the guidance of

management (and within the confines of the firm’s culture). As shown in Figure 7, corporate control

could be extended over the entire generation, selection, and implementation process or over just one or

two parts. What determines when corporations should control each part?

The basic theory of transaction cost economics states that company control is used in place of markets

only when the conditions are absent for a well operating market, and the cost of corporate control (what

is given up by managing the process) is not too high. Markets are the preferred option because markets

are amazing – nearly magical – at creating efficient solutions. In the best of all worlds, firms could access

designers anywhere on the globe to get new ideas and select the best design (from a price performance

point of view). What might prevent such an efficient market? We have already introduced several

reasons. In this section we focus on two of the most important ones: the disclosure problems and the

coordination problem.


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Figure 7 – Four possible governance boundaries for organizing innovative activity. Market transactions occur

at the boundary of the yellow boxes.

Solutions to the disclosure problem

The contracting problem is caused by the difficulty of being able to buy or sell an idea. Suppose a

designer has an idea for an innovative new product and that the idea is obviously a good one. Given its

obvious merit, the designer may fear revealing it to a potential buyer because the buyer might simply

take it and implement it himself. To get around this contracting problem, most governments provide

designers with the opportunity to gain an official property right to practical ideas in the form of a patent.

To be patentable, ideas must be new, useful, non-obvious.. The large majority of ideas cannot be

patented because they are artistic (e.g. the shape of a jacket) or not new (e.g. precise combinations or

mixtures cannot usually be patented) or not-obvious. For ideas which cannot be patented, designers face

the ―disclosure problem‖. To allow a potential of an idea a chance to assess its value, the designer must

reveal the idea. If they do, however, there is nothing to prevent the user from taking the idea without

paying.

The most common solution to the disclosure problem is for the designer to use the idea himself. For

example, if the designer invents a better way to paint cars they can decide to enter the car-painting

business rather than try to sell their idea to an existing company. This transforms the contract between

inventor from one based on a disembodiment idea to one based on material product or a service. By

imbedding the idea in a product or service, the inventor can better keep it hidden and thus can gain a

greater ongoing reward.

For firms seeking to gain innovations from outside designers, there are a couple of possible solutions to

the contracting problem. First, before the idea is even generated, the firm may decide to hire the inventor

to provide ongoing design services for some period of time. In essence, the firm takes a bet on the

designer’s future innovation and buys this expected stream of ideas. The designer is willing to enter into

such an agreement because doing so reduces their risk (they get a salary rather than the expectation of

uncertain cash flows). The buyer covers any needed investment and pays the designer for their work (not

for a particular idea). In essence, the buyer is providing insurance to the designer in exchange for some

of the potential value of the designs they may generate. In some cases, the contract may be for a

particular term of work or it may be an ongoing relationship (e.g. an employer/employee).

There are drawbacks to this approach. Obviously, corporate oversight and management narrows the

range of innovation sources because the number of individuals involved in design is constrained. In

addition, if the locus of design knowledge is not known, the buyer must now evaluate the quality of the

designer (rather than the design) and this may be more difficult. Finally, once they are fully insured from

loss (they invest no money and they are paid for their effort) designers may lose some of their motivation

to invent the truly best design.

The problems inherent in hiring the designer rather than buying the design are emblematic of the types of

tradeoffs involved when contract problems exist. Cost caused by lost incentives must be weighed against


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costs caused by difficult market contracting. Isn’t there some third way around this problem? In a few

cases, designers and buyers may be able to contract on the idea by developing a reputation for fair

dealing. Companies like SC Johnson benefit from being able to access a continuous stream of innovative

product ideas. Many of these ideas are not protected by strong property rights and this should make it

difficult for designers and buyers to contract on an idea. However, given the need for this stream of

ideas, a firm like SC Johnson may be able to claim credibly that they will be honest in their dealings with

inventors. If they fail to act honestly now (and this fact became known) other designers will avoid them

and they will lose a valuable source of ideas. Something similar happens when newspapers source ideas

from freelance journalists. News ideas cannot be protected with intellectual property so a writer has

little protection for his/her idea. By establishing a reputation for fair dealing, some papers are able to

attract better storiesvi.

Solutions to the Coordination Problem

Coordinating problems caused by multiple interdependent designs also caused difficult contractual

problems between designers and implementers. Interdependencies may allow the potential for ―hold

up‖ and other types of strategic maneuvering. Suppose, for example that an implementer hires and

external designer to perform a critical part of a design task and then invests in design for all of the other

interdependent elements which will make the final product work. This makes the value of the critical

component extraordinarily high because without it the investment is worthless and the future returns

zero. Thus, the contracted designer could have an incentive to ―hold up‖ the implementer and ask for a

larger piece of the overall rewards.

The holdup problem can also work in the other direction. If the inventor invests to create the design, and

that design is interdependent with other elements of design which the implementer controls, the

implementer may have an incentive to return to the designer and ask for a reduction in the fee for the

design. The designer is left with two choices, take the reduced price or get nothing (because their

invention is worthless without the other elements from the implementer).

Because of these contracting problems, design and implementation of interdependent elements often

occurs within a corporate structure. Designers are higher as service providers (or employees) and work

at the direction of managers. Managers determine the interdependencies among projects and carefully

monitor the design project for potential conflicts. Project management tools like critical path analysis and

PERT help managers determine whether time interdependencies could cause difficulties. Organizational

designs and philosophies – such as ―Heavy Weight Project Managers‖ -- are often used to allow

managers to solve problems caused by interdependencies as they emerge.

In recent years, open source software development has highlighted another solution to the problem. In

open source projects, modular design, artifacts, and customs help correct for the problems created by

interdependent design. These systems allow a hybrid form of innovation organization to emerge -- one

that provides some of the advantages of a hierarchy and some of the advantages of a market. Open

source projects include some hierarchical control (e.g. a group of lead designers who make final decisions


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about what code is included in releases), but it also includes many of the elements a market. Ideas are

submitted freely and selection occurs in a distributed manner (as is the case when customers select by

buying a product). How are distributed design and decision-making possible when there is such need for

coordinating interdependencies across design elements?

Most open-source projects include elements reduce interdependencies or allow their control.

Reduced Interdependencies

Modular Design helps reduce the problem of holdup and the need for coordination by simply

reducing the degree to which any element of the design is dependent on any other. By making each

element of the overall design both small and independent, the potential for holdup is restricted. In

addition, open source communities usually stipulate the use of an open access property right (e.g. the

General Public License) which effectively precludes the potential for a designer to holdup the overall

effort.

Control of Interdependencies

Artifacts support coordination across different design efforts by constraining the behavior of

designers. Most computer programmers use an Integrated Development Environment (IDE) when

writing computer code. These environments require software to be structured in common ways – and

this facilitates coordination of interdependencies.

Customs constrain behavior by providing implicit sanctions if a designer or the implementer acts

in an unfair manner. In open source communities, a substantial amount of the communication between

individuals pertains to what the community perceives to be appropriate behavior. Failure to abide by

these norms may severely restrict an individual designer’s access to benefits that the community

providesvii.

Human intervention and arbitration provides the final layer of coordination in open source

projects. The initial kernel of open source projects is often written by one or two people, and thus

interdependencies are handled internally. Code that is submitted later to the project is not accepted into

the operating base of programs until it has been tested and vetted. If it is found to conflict with existing

code, it is not permitted to remain in the main code base. In some cases, the people responsible for this

final decision are part of a corporate organization (e.g. Red Hat or Sun Microsystems). In many cases, the

final arbitration on open source projects occurs within a non-profit organization (e.g. the Mozilla

Foundation for Firefox).


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Figure 8 shows how common innovation structures commonly map to two common problems (contracting and

coordination)in sourcing innovation.

Figure 8 shows a representation of the two cases of contracting problem. As predicted by transaction cost

economics, the greater the contracting problems, the more likely design will be organized within a

hierarchical structure like a firm. When contracting is easier, design tournaments like the one used by

Innocentive are more likely to be used. In between, intermediate forms are more common. In the lower

right quadrant, where disclosure problems exist but coordination problems are relatively mild, a firm

may choose to specify a use requirement and then contract directly with a designer for the creation of a

potential solution. This is what firms do when they work with a company like IDEO. In the upper left

corner where disclosure problems are relatively benign but coordination problems are more challenging,

intermediate hierarchical structure like those used in open source are more common. These intermediate

forms include systems for controlling and negotiating interdependencies. They allow for a central group

to make final decisions. But, they also limit the power of this central hierarchy and allow for some of the

benefits of a marketviii.

Trends in Governance and their Origins

A perusal of the popular press would suggest that the prevalence of innovation markets and intermediate

forms of innovation organization (e.g. open source) has increased in the past decade. New terms such as

open innovation, distributed innovation, and user innovation have become commonplace. What may

explain this apparent trend?

The current trend may have a longer history than is commonly understood, thus making the recent

uptick appear all the more dramatic. As discussed earlier, user innovation has an ancient history. Firms


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have also long sought to acquire innovation from external sources. The color television was invented (in

the U.S.) by Philo T Farnsworth but hardly anyone remembers that because RCA bought the rights to the

design for $1 million. Yet, despite such precedents, there are several reasons why a trend toward more

distributed innovation may be real. Economic and technological development has influenced some of the

key variables discussed in this module. In each case, these changes have increased the value of more

distributed forms of innovation.

Economic development across the globe has increased the distribution of design capabilities by increasing

the number of talented individuals who can engage in innovation work. This trend is continuing. Each

year, China and India graduate almost one million engineers (compared to only about 70,000 in the

United States). Even if such numbers are corrected for differences in quality, each year adds to the

diffusion of design talent around the globe.

Electronic communication has reduced the cost of accessing these distributed sources of innovation.

Specifications for design tournaments can be broadcast more easily. Rich modes of communication are

available even when exchange is carried out at a distance. Thus, the combination of greater distribution

of designers and users plus eased communication has changed the boundaries between different types of

structures.

More distributed design knowledge has made it more valuable to run design tournaments – even when

some designers are lost because they fear that their ideas may be appropriated. The growing value of

managing external design competitions has also caused the emergence of support systems which help to

allay some of the appropriability risks of weakly-protected intellectual property. Innocentive, Topcoder,

and other firms represent credible third parties which can facilitate contracting between designers and

implementers.

More distributed use knowledge makes accessing user communities to evaluate ideas for innovation

more valuable. More distributed users often have more heterogenous needs because they operate in

different physical, economic, and social environments. Tapping into this diverse user environment can

allow the firm to test the value of their designs in these multiple contexts. Each different user is

essentially a small evaluation experiment. When users are also designers, the value of distributed

selection becomes even greater. Users with private information about local contexts can directly develop

solutions for these contexts.

The operational advantages caused by the increases in use knowledge mean that systems for resolving

the governance problems have emerged. Organizations like source forge have emerged to help facilitate

collaborated governance of open source projects. Design systems for controlling and testing for

interdependence…


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Figure 9 shows how changes in the diffusion of design capabilities and use knowledge are influencing the

prevalence of common innovation structures.

As shown in Figure 9, the net result of all of these changes is that the range where organizing innovation

within a firm has shrunk. Yet, it is important to remember that Figure 9 is not drawn to scale. A large

amount of innovation – perhaps still the majority of innovation – occurs within an integrated firm which

both does the innovation and implementation of the solution.


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1. Pedagogical Plan for the Module

Sections and Cases

The module is designed to be taught in order. Generally, instructors have chosen to begin with the IDEO

case and then move to Team New Zealand, but reversing the order could also make sense. Both cases

usually generate very high energy classes and get the module off to a good start. The next two cases are

also fairly high energy affairs. In particular, some classes have found the Threadless case to be very

provocative. The module is designed for Innocentive to precede Threadless, but the order could be

reversed if logistics required. Because the Threadless case is designed to be experienced online,

instructors need to be conscious that students must have high speed access to the internet. Video tapes of

representatives speaking at HBS may be available.

The most difficult of the cases in the module is the Radical Collaboration at IBM. This case requires

students to get a passing knowledge of the fabrication of semiconductors. Deep technological knowledge

is not needed, but nevertheless some students may find the terminology and technology daunting.

Providing an introduction in a previous class may help alleviate some of this anxiety. In addition, sites

like YouTube often have videos of the manufacturing process.

Part 1: Managing Generation and Selection

This section emphasizes the importance of the generation and selection process and introduces some of

the operations analysis which might inform managerial decision-making.

IDEO

o Central concepts:

The value of variability in generation

o Innovation often entails trial and error

o Enlightened trial and error succeeds over the planning of loan genious

o The value of trying wild ideas

o Techniques for increasing the variety of ideas generated

o Case Wrap Slides


________________________________________________________________________________________________________________

31

Innovation Management

• Innovation is a leap into an undiscovered world.

It often involves a process of discovery that is in

part trial and error.

– Enlightened trial and error succeeds over the

planning of lone genius.

• To find potential innovations, you have to have

– A means of generating alternatives

– A way of selecting among alternatives

A Famous Example

• "Before I got through," he recalled, "I tested no fewer than 6,000 vegetable growths, and ransacked the world for the most suitable filament material."

• "Genius is one percent inspiration and ninety-nine percent perspiration.“

• “I didn’t fail. I just discovered 9999 ways NOT to invent the light bulb.”– Thomas Edison

Illustration from U.S.

Patent #223898:

Electric-Lamp.

Issued January 27,

1880 to Thomas

Edison.

Idea Generation

0

1

Va

lue

of

Bes

t A

lte

rna

itve


Expected Value

Greater

Variance of

Underlying

Distribution

Mean of

Distribution

Principles of Alternative Generation:

Exploration

• Exploring more ideas yield better solutions.– The larger the variance in ideas the more

valuable additional tests

– The marginal value of each additional test decreases

• Strategies for increasing variance.– Encourage creativity within a given set of

designers

– Draw from a population of designers with a large variance

IDEO

More

alternatives

tested

More variance in

the alternatives

tested

Better

Innovations

Organize for rapid

design iterations

Hire a diverse team

Fail early and often

Leverage the

value of early

information

Encourage

experimentation

$

Prototype often to

test ideas

“Kindergarten”

environment


________________________________________________________________________________________________________________

32

Team New Zealand

o Concepts

Rough landscapes and alternative generation

o Search strategies in rough landscapes

Managing Generation and Selection

o Introduction to stopping rules

o The sources of cost in evaluation

o Strategies for reducing the cost of evaluation

The design funnel

o Introduction to the metaphor

o Heuristics for the location of funnel gates

Innovation systems can produce competitive advantage.

o Case Wrap Slides:

Design as Trial and Error

• At some level, most innovation

spaces are rough landscapes.

• To find the best solution, you

have to have

– boundary conditions for the

solution

– a means of generating

alternatives

– a criteria for selecting among

alternatives

0

2

4

6

8

10

8-10

6-8

4-6

2-4

0-2

Exploring a Design Space• Experiment-driven development is all about narrowing

the search space for a good design

– Experience allows the space to be bounded and basic design ideas to be selected.

– Computer simulation allows generation of design alternatives.

• TNZ reduced testing costs so as to test of more alternatives– Integrating designers and users

• Putting the designers and the users in the same place.

– Make the tests faster and more accurate• Two identical boats speeds experimental cycle.

• Having the computer systems on the doc allows faster feedback.

• Testing in San Diego allows more accurate tests.

Strategies for Reducing Selection

Costs• Reduce lost development time

– Verify objectives

– Test early - use computers to pre-test designs

– Locate designers near users – designers on the dock

– Run tasks in parallel if possible

• Make tests accurate– Fidelity

• Is your test a good proxy for your objective function?

• Yes, real boat speed is what we need.

– Identification• Are you testing what you think you are testing?

• Yes, the only difference between the two boats is the keel.

– Representativeness• Are you testing it in the right conditions?

• Yes, tests done on location

– Precision/noise• How many tests are required to obtain accurate results?

• Not many. The two boats race in the same conditions.

Exploring a Design Space• Experiment-driven development is all about narrowing

the search space for a good design

– Experience allows the space to be bounded and basic design ideas to be selected.

– Computer simulation allows generation of design alternatives.

• TNZ reduced testing costs so as to test of more alternatives– Integrating designers and users

• Putting the designers and the users in the same place.

– Make the tests faster and more accurate• Two identical boats speeds experimental cycle.

• Having the computer systems on the doc allows faster feedback.

• Testing in San Diego allows more accurate tests.


________________________________________________________________________________________________________________

33

0

1

Valu

e o

f B

est

Alt

ern

ait

ve


Expected Value

Marginal Value of Additional AlternativeCost of Assessment

Choosing when to stop

Stop

Part 2: Managing the Locus of Innovation

In this section, we consider two cases where managers must consider the locus of innovation. In the first

case, Innocentive, companies have knowledge about the merits of potential solutions and so can act as the

selector of good ideas. However, they lack knowledge of the best place to source good ideas. As a result,

they use the Innocentive tournament system to broadcast the need for their ideas.

In the second case, Threadless, a company

Innocentive

o Concepts

Introduction to Distributed Innovation

Conditions for Distributed Innovation

o Knowledge about the locus of innovation

o Increase number and variance of ideas by going to a diverse outside pool

o Mechanism for appropriating value from idea.

Introduction to the difficult of contracting on ideas – Arrows paradox

o The disclosure problem or Arrow’s paradox

o Potential solutions to the disclosure problem

Role of intermediaries like Innocentive

o Reduce the cost of finding solvers

o Reduce the cost of specifying what is needed

o Make it easier to enforce agreements between solver and seeker?

CaseWrap Slides:


________________________________________________________________________________________________________________

34

InnoCentive

• Selection and generation:– Generation:

• InnoCentive allows seekers to increase the number and variance of ideas the consider by going to a diverse outside pool.

– Selection: • InnoCentive users have application knowledge and so can effectively

select good solutions.

• Governance:– It is often hard to buy and sell ideas. InnoCentive intervenes to

make it easier.• Reduces the cost of finding solvers

• Reduces the cost of specifying what is needed

• Make it easier to enforce agreements between solver and seeker.

Conditions for InnoCentive Model

• Seekers

– Less relevant knowledge and capabilities

– Have a mechanisms to benefit from the innovation

– Have ability to select better solutions

• Problem characteristics

– Problems are definable, codifiable, and require little investment

• Solvers

– Have an incentive to provide the solution

• Disclosure of the solution is better than independent commercialization.

– Have extensive capabilities or knowledge

• Intermediary

– Can effectively reduce the cost of defining problems, finding

solvers, contracting on solutions, and monitoring and enforcing

contracts.

Markets for Ideas (Innocentive)

• Advantages:

– Generation:

• Allow seekers to increase the number and variance of ideas the consider by going to a

diverse outside pool.

– Selection:

• Users have application knowledge and so can effectively select good solutions.

• Problems:

– It is often hard to buy and sell ideas.

• Disclosure problems, specifying what is needed, monitoring, enforcement.

• InnoCentive

– Intervenes to make facilitate a market for ideas.

• Reduces the cost of finding solvers

• Reduces the cost of specifying what is needed

• Make it easier to enforce agreements between solver and seeker.

Threadless

o Concepts

Distributed selection

o The value of variability in selection

o Methods for accessing early selection

Multisided platforms

o Role of the platform as governance system

o Incentive compatibility

o Profit taking

Disclosure problem

o Secrecy

o Spillover value (reputation etc.)

o Figures


________________________________________________________________________________________________________________

35

Threadless Innovation Platform

Designers Users

Customers

Threadless T-shirt Company

What’s cool

(and thus

cool to buy).What people buy.

Designs and Demand info.

Shirts InDelivered

Printed

Shirts Out

Rapid feedback on

what’s cool and how

to improve designs.

Distributed Innovation and

Selection (Threadless)• Distributed solution generation

• Benefit: More diverse and numerous alternatives

• Problems: Disclosure (Arrow’s paradox)

Coordination of Interdependencies

• Distributed selection• Benefit: Accurate selection of best ideas for users

• Problem: Misaligned incentives with selectors

• Community generation and selection• Benefit: Learning and improvement

• Problem: Limits flexibility and strategic options for innovation

platform

Innovation and Selection:

Location Typology

Locus of Use Knowledge

Known Unknown

Lo

cu

s o

f S

olu

tio

n

Kn

ow

led

ge

Unknown

Known

ElectedSelected

Market

Feedback

(Zara)

Tournament

(Innocentive)

Internal

(TNZ)

Community

(Threadless)

managed

Invited

Designs Are


________________________________________________________________________________________________________________

36

Part 3: Selecting the Institutional Form

Radical Collaboration

o Concepts

EOS and Shakeout

Need for coordination across multiple players

Governance

o Figures

Distributed Innovation and

Selection• Distributed solution generation

• Benefit: More diverse and numerous alternatives

• Problems: Disclosure (Arrow’s paradox)

Coordination of Interdependencies

• Distributed selection• Benefit: Accurate selection of best ideas for users

• Problem: Misaligned incentives with selectors

• Community generation and selection• Benefit: Learning and improvement

• Problem: Limits flexibility and strategic options for innovation

platform

Governance of Innovation

Disclosure ProblemLow High

Co

ord

inatio

n P

roble

m

High

Low

Internal R&D

(Team NZ)

Contract on

Design

(Threadless)

Contract with

Designer

(IDEO)

Collaborative

Governance

(Open Source)


________________________________________________________________________________________________________________

37

References

Dahan, Ely, and Mendelson, Haim. ―An Extreme-Value Model of Concept Testing.‖ Management Science

47 (January 2001): 102-116.

Evenson, Robert E., and Kislev, Yoav. ―A Stochastic Model of Applied Research.‖ The Journal of Political

Economy. 84 (April 1976): 265-282.

Girorta, Karan, Christian Terwiesch, and Karl T. Ulrich. ―Valuing R&D Projects in a Portfolio: Evidence

from the Pharmaceutical Industry.‖ Management Science 53 (September 2007): 1452-1466.

Loch, Christoph H., Christian Terwiesch, and Stefan Thomke. ―Parallel and Sequential Testing of Design

Alternatives.‖ Management Science 47 (May 2001): 663-678.

Nelson, Richard R. ―Uncertainty, Learning, and the Economics of Parallel Research and Development

Efforts.‖ The Review of Economics and Statistics. 43 (November 1961): 351-364.

Taylor, Randall, Christian Terwiesch, and Karl T. Ulrich. ―User Design of Customized Products.‖

Marketing Science 26 (March-April 2007): 268-282.

Terwiesch, Christian, and Xu, Yi. ―Innovation Contests, Open Innovation, and Multiagent Problem

Solving.‖ Management Science 54 (September 2008): 1529-1543.

Thomke, Stefan, and Bell, David E. ―Sequential Testing in Product Development.‖ Management Science 47

(February 2001): 309-323.


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38

EndNotes

iEgan, Louise and Betts, Louise, Thomas Alva Edison: The Great American Inventor Published Barron's Educational Series, 1987 ii The conceptual problem of finding the best solution in a rough landscape has been of great interest to engineers and computer

scientists. Depending on what is known about the underlying landscape, different search approaches may be optimal. The vast majority include a combination of large jumps to different initial starting points and then a system for repeatedly guessing superior locations. A commonly used and robust example is the Nelder-Mead Simplex Method. Nelder-Mead employs a simplex of points (in two dimensions this would be a triangle) to explore the slope of the solution space. It then uses the local shape of the surface to compute a position which may be better than any of the three. It tests this position and if it is superior replaces the worst point in the simplex with the new position. Unfortunately, like all general purpose optimization methods, it can get stuck on local peaks (a local optimum). To find better solutions, the simplex must be place in some new location and the algorithm restarted. iii Mathematically, these cases can be models using different types of density functions for the returns: Frechet (great upside),

Weibull (limited upside), and Gumbell (no limits in either direction). iv There are many guides to managing new product implementation. For example, see: Ulrich, Karl T. and Eppinger, Steven D (2004) Product Design and Development, 3rd Edition, McGraw-Hill, New York, 2004

v A classic example, is the case of the recipe for Coca Cola. Because the recipe is simply a mixture of everyday components, it

cannot be patented. It is not an expression (like a piece of artwork or an essay), so it cannot be protected with a copyright. As a result, the company chooses to make the syrup themselves so as to keep the precise recipe secret. vi In some cases, a third party might be able to act to facilitate contracting between the inventor and the implementer. This agent could assess the value of the design and then report it to the implementer. Because this agent wishes to retain a reputation for fair dealing, the value of the reputation will exceed any value they might get from defecting and using the idea themselves. Use of such third parties makes sense when contracts between inventors and implementers is infrequent. vii The use of customs and artifacts to coordinate economic exchange has an ancient history. Open source differs from precedence in

the speed and scope of these norms. To be understandable, software code must be organized in an agreed upon manner –

definitions, data, processing, all must be specified and organized in agreed upon ways. These common understandings of how data

should be organized are constantly under negotiation. As technology or problems change new common architectures emerge.

viii A note on thee IP protection within Open Source projects. In Figure 8, we place open source projects in the quadrant where the

contracting problem is low and the coordination problem is high. Clearly, with thousands of people contributing elements of one

interdependent structure, interdependency is a critical issues. Why do we also say that the contracting problem is ―low‖? Open

source code is often written under software licenses which stipulate that the code is freely available. Wouldn’t this make it very

difficult to contract on the code? While it is true that the code itself cannot usually be protected in an open source project, the

benefit that the code provides usually can be. Many designers of open source code are seeking a solution to a local problem. For

example, they need to make it possible for users to interface to a particular physical device. By writing an interface, the designers

raise the value of this device, and while they cannot directly benefit from the code, they can benefit from the additional sales of the

device. In other cases, the designer may benefit from the use of the code. For example, a designer may make his or her job easier by

designing a system for allowing automatic maintenance of computer systems.

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