创新者的解答：创造与维持成功增长

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"The Innovator's Solution (English Version)" 是一本由 Clayton M. Christensen 和 Michael E. Raynor 合著的书籍，该书探讨了如何识别并利用颠覆性创新来推动市场的变革，同时提供了避免自己被颠覆的策略。这本书是继 Christensen 的畅销书 "The Innovator's Dilemma" 之后的又一力作，旨在帮助管理者适应动态世界的挑战，调整他们的战略。书中，作者深入分析了那些能够超越现有竞争者、实现快速增长的"颠覆者"公司。他们通过九个关键的商业决策来阐述增长的核心要素，这些决策涉及公司的市场定位、产品开发、目标客户选择、业务范围、避免商品化、组织能力、战略制定过程、投资类型以及高层领导在推动新增长中的角色。 - 第一章：增长的必要性这部分讨论了为什么企业必须追求持续增长，并且如何将增长作为企业的核心战略。 - 第二章：如何打败最强大的竞争对手？作者揭示了如何通过颠覆性创新来颠覆市场格局，超越传统巨头。 - 第三章：顾客会想要购买什么产品？这一章探讨了如何预测和满足未来消费者的需求，以创造新产品或服务。 - 第四章：谁是我们产品的最佳客户？确定目标客户群体是成功的关键，本章解释了如何定位最有利可图的市场细分。 - 第五章：如何确定业务的范围？在不断变化的环境中，正确设定业务边界是避免被边缘化的关键。 - 第六章：如何避免商品化？书中提出了如何通过创新和独特价值主张来防止产品和服务变得同质化。 - 第七章：你的组织有能力实现颠覆性增长吗？这一部分探讨了组织结构和文化如何影响企业的创新能力和颠覆性增长的可能性。 - 第八章：管理战略制定过程描述了如何系统性地制定和执行有效战略，以支持持续增长。 - 第九章：好钱与坏钱讨论了不同类型的投资如何影响企业的长期发展。 - 第十章：高层管理者在引领新增长中的角色高层领导者如何通过愿景、决策和资源分配来驱动创新和增长。 - 尾声：交接接力棒关注领导力的传承和下一代创新者的重要性。此外，书中有详细的图表和表格，帮助读者更直观地理解各个概念。这本书对于那些希望在创新管理和产品管理领域寻求深入见解的专业人士来说是一份宝贵的资源。通过学习书中的理论和案例，读者可以更好地理解和应对创新带来的机遇与挑战，从而推动企业的成功增长。

they reside in that particular job.

The process of sorting through and packaging ideas into plans that can win funding, in other words, shapes

those ideas to resemble the ideas that were approved and became successful in the past. The processes

have in fact evolved to weed out business proposals that target markets where demand might be small. The

problem for growth-seeking managers, of course, is that the exciting growth markets of tomorrow are small

today.

This is why the senior managers at the major toy company and at BIG can live in the same world and yet see

such different things. In every sizable company, not just in the toy business, the set of ideas that has been

processed and packaged for top management approval is very different from the population of ideas that is

bubbling at the bottom.

A dearth of good ideas is rarely the core problem in a company that struggles to launch exciting new-growth

businesses. The problem is in the shaping process. Potentially innovative new ideas seem inexorably to be

recast into attempts to make existing customers still happier. We believe that many of the ideas that emerge

from this packaging and shaping process as me-too innovations could just as readily be shaped into

business plans that create truly disruptive growth. Managers who understand these forces and learn to

harness them in making key decisions will develop successful new-growth businesses much more

consistently than historically has seemed possible.

[14]

[12]

The scholars who introduced us to these forces are Professor Joseph Bower of the Harvard Business

School and Professor Robert Burgelman of the Stanford Business School. We owe a deep intellectual debt to

them. See Joseph L. Bower, Managing the Resource Allocation Process (Homewood, IL: Richard D. Irwin,

1970); Robert Burgelman and Leonard Sayles, Inside Corporate Innovation (New York: Free Press, 1986);

and Robert Burgelman, Strategy Is Destiny (New York: Free Press, 2002).

[13]

Clayton M. Christensen and Scott D. Anthony, hat’s the BIG Idea??Case 9-602-105 (Boston: Harvard

Business School, 2001).

[14]

We have consciously chosen phrases such as ncrease the probability of success?because business

building is unlikely ever to become perfectly predictable, for at least three reasons. The first lies in the nature

of competitive marketplaces. Companies whose actions were perfectly predictable would be relatively easy to

defeat. Every company therefore has an interest in behaving in deeply unpredictable ways. A second reason

is the computational challenge associated with any system with a large number of possible outcomes.

Chess, for example, is a fully determined game: After White’s first move, Black should always simply resign.

But the number of possible games is so great, and the computational challenge so overwhelming, that the

outcomes of games even between supercomputers remain unpredictable. A third reason is suggested by

complexity theory, which holds that even fully determined systems that do not outstrip our computational

abilities can still generate deeply random outcomes. Assessing the extent to which the outcomes of

innovation can be predicted, and the significance of any residual uncertainty or unpredictability, remains a

profound theoretical challenge with important practical implications.

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Where Predictability Comes From: Good Theory

The quest for predictability in an endeavor as complex as innovation is not quixotic. What brings predictability

to any field is a body of well-researched theory— contingent statements of what causes what and why.

Executives often discount the value of management theory because it is associated with the word theoretical,

which connotes impractical. But theory is consummately practical. The law of gravity, for example, actually is

a theory— and it is useful. It allows us to predict that if we step off a cliff, we will fall.

[15]

Even though most managers don’t think of themselves as being theory driven, they are in reality voracious

consumers of theory. Every time managers make plans or take action, it is based on a mental model in the

back of their heads that leads them to believe that the action being taken will lead to the desired result.

[16]

The

problem is that managers are rarely aware of the theories they are using— and they often use the wrong

theories for the situation they are in. It is the absence of conscious, trustworthy theories of cause and effect

that makes success in building new businesses seem random.

To help executives to know whether and when they can trust the recommendations from management books

or articles (including this one!) that they read for guidance as they build their businesses, we describe in the

following sections a model of how good theories are built and used. We will repeatedly return to this model to

illustrate how bad theory has caused growth builders to stumble in the past, and how the use of sound theory

can remove many of the causes of failure.

[17]

How Theories Are Built

The process of building solid theory has been researched in several disciplines, and scholars seem to agree

that it proceeds in three stages. It begins by describing the phenomenon that we wish to understand. In

physics, the phenomenon might be the behavior of high-energy particles. In the building of new businesses,

the phenomena of interest are the things that innovators do in their efforts to succeed, and what the results of

those actions are. Bad management theory results when researchers impatiently observe one or two success

stories and then assume that they have seen enough.

After the phenomenon has been thoroughly characterized, researchers can then begin the second stage,

which is to classify the phenomenon into categories. Juvenile-onset versus adult-onset diabetes is an

example from medicine. Vertical and horizontal integration are categories of corporate diversification.

Researchers need to categorize in order to highlight the most meaningful differences in the complex array of

phenomena.

In the third stage, researchers articulate a theory that asserts what causes the phenomenon to occur, and

why. The theory must also show whether and why the same causal mechanism might result in different

outcomes, depending on the category or situation. The process of theory building is iterative, as researchers

and managers keep cycling through these three steps, refining their ability to predict what actions will cause

what results, under what circumstances.

[18]

Getting the Categories Right

The middle stage in this cycle— getting the categories right— is the key to developing useful theory. To see

why, imagine going to your medical doctor seeking treatment for a particular set of symptoms, and before

you have a chance to describe what ails you, the physician hands you a prescription and tells you to ake

two of these and call me in the morning.?

ut how do you know this will help me??you ask. haven’t told you what’s wrong.?

hy wouldn’t it work??comes the reply. t cured my previous two patients just fine.?

No sane patient would accept medicine like this. But academics, consultants, and managers routinely

dispense and accept remedies to management problems in this manner. When something has worked for a

few xcellent?companies, they readily advise all other companies that taking the same medicine will be

good for them as well. One reason why the outcomes of innovation appear to be random is that many who

write about strategy and management ignore categorization. They observe a few successful companies and

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then write a book recommending that other managers do the same things to be successful too— without

regard for the possibility that there might be some circumstances in which their favorite solution is a bad idea.

[19]

For example, thirty years ago many writers asserted that vertical integration was the key to IBM’s

extraordinary success. But in the late 1990s we read that non-integration explained the triumph of

outsourcing titans such as Cisco and Dell. The authors of est practices?gospels such as these are no

better than the doctor we introduced previously. The critical question that these researchers need to resolve

is, hat are the circumstances in which being integrated is competitively critical, and when is a strategy of

partnering and outsourcing more likely to lead to success??

Because theory-building scholars struggle to define the right and relevant categorization of circumstances,

they rarely can define the circumstances immediately. Early studies almost always sort researchers’

observations into categories defined by the attributes of the phenomena themselves. Their assertions about

the actions or events that lead to the results at this point can only be statements about correlation between

attributes and results, not about causality. This is the best they can do in early theory-building cycles.

Consider, for illustration, the history of man’s attempts to fly. Early researchers observed strong correlations

between being able to fly and having feathers and wings. Possessing these attributes had a high correlation

with the ability to fly, but when humans attempted to follow the est practices?of the most successful flyers

by strapping feathered wings onto their arms, jumping off cliffs, and flapping hard, they were not successful—

because as strong as the correlations were, the would-be aviators had not understood the fundamental causal

mechanism that enabled certain animals to fly. It was not until Bernoulli’s study of fluid mechanics helped

him articulate the mechanism through which airfoils create lift that human flight began to be possible. But

understanding the mechanism itself still wasn’t enough to make the ability to fly perfectly predictable. Further

research, entailing careful experimentation and measurement under various conditions, was needed to

identify the circumstances in which that mechanism did and did not yield the desired result.

When the mechanism did not result in successful flight, researchers had to carefully decipher why— what it

was about the circumstances in which the unexpected result occurred that led to failure. Once categories

could be stated in terms of the different types of circumstances in which aviators might find themselves, then

aviators could predict the conditions in which flight was and was not possible. They could develop

technologies and techniques for successfully flying in those circumstances where flight was viable. And they

could teach aviators how to recognize when the circumstances were changing, so that they could change

their methods appropriately. Understanding the mechanism (what causes what, and why) made flight

possible; understanding the categories of circumstances made flight predictable.

[20]

How did aviation researchers know what the salient boundaries were between these categories of

circumstance? As long as a change in conditions did not require change in the way the pilot flew the plane,

the boundary between those conditions didn’t matter. The circumstance boundaries that mattered were those

that mandated a fundamental change in piloting techniques in order to keep the plane flying successfully.

Similar breakthroughs in management research increase the predictability of creating new-growth

businesses. Getting beyond correlative assertions such as ig companies are slow to innovate,?or n our

sample of successful companies, each was run by a CEO who had been promoted from within,?the

breakthrough researcher first discovers the fundamental causal mechanism behind the phenomena of

success. This allows those who are looking for n answer?to get beyond the wings-and-feathers mind-set of

copying the attributes of successful companies. The foundation for predictability only begins to be built when

the researcher sees the same causal mechanism create a different outcome from what he or she expected—

an anomaly. This prompts the researcher to define what it was about the circumstance or circumstances in

which the anomaly occurred that caused the identical mechanism to result in a different outcome.

How can we tell what the right categorization is? As in aviation, a boundary between circumstances is salient

only when executives need to use fundamentally different management techniques to succeed in the different

circumstances defined by that boundary. If the same statement of cause and effect leads to the same

outcome in two circumstances, then the distinction between those circumstances is not meaningful for the

purposes of predictability.

To know for certain what circumstances they are in, managers also must know what circumstances they are

not in. When collectively exhaustive and mutually exclusive categories of circumstances are defined, things

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get predictable: We can state what will cause what and why, and can predict how that statement of causality

might vary by circumstance. Theories built on categories of circumstances become easy for companies to

employ, because managers live and work in circumstances, not in attributes.

[21]

When managers ask questions such as oes this apply to my industry??or oes it apply to service

businesses as well as product businesses??they really are probing to understand the circumstances. In our

studies, we have observed that industry-based or product/ service-based categorization schemes almost

never constitute a useful foundation for reliable theory. The Innovator’s Dilemma, for example, described how

the same mechanism that enabled entrant companies to up-end the leading established firms in disk drives

and computers also toppled the leading companies in mechanical excavators, steel, retailing, motorcycles,

accounting software, and motor controls.

[22]

The circumstances that mattered were not what industry you

were in. Rather, there was a mechanism— the resource allocation process— that caused the established

leaders to win the competitive fights when an innovation was financially attractive to their business model. The

same mechanism disabled the established leaders when they were attacked by disruptive innovators— whose

products, profit models, and customers were not attractive.

We can trust a theory only when its statement of what actions will lead to success describe how this will vary

as a company’s circumstances change.

[23]

This is a major reason why the outcomes of innovation efforts have

seemed quite random: Shoddy categorization has led to one-size-fits-all recommendations that in turn have

led to the wrong results in many circumstances.

[24]

It is the ability to begin thinking and acting in a

circumstance-contingent way that brings predictability to our lives.

We often admire the intuition that successful entrepreneurs seem to have for building growth businesses.

When they exercise their intuition about what actions will lead to the desired results, they really are

employing theories that give them a sense of the right thing to do in various circumstances. These theories

were not there at birth: They were learned through a set of experiences and mentors earlier in life.

If some people have learned the theories that we call intuition, then it is our hope that these theories also can

be taught to others. This is our aspiration for this book. We hope to help managers who are trying to create

new-growth businesses use the best research we have been able to assemble to learn how to match their

actions to the circumstances in order to get the results they need. As our readers use these ways of thinking

over and over, we hope that the thought processes inherent in these theories can become part of their

intuition as well.

We have written this book from the perspective of senior managers in established companies who have been

charged to maintain the health and vitality of their firms. We believe, however, that our ideas will be just as

valuable to independent entrepreneurs, start-up companies, and venture capital investors. Simply for purposes

of brevity, we will use the term product in this book when we describe what a company makes or provides.

We mean, however, for this to encompass product and service businesses, because the concepts in the

book apply just as readily to both.

[15]

The challenge of improving predictability has been addressed somewhat successfully in certain of the

natural sciences. Many fields of science appear today to be cut and dried— predictable, governed by clear

laws of cause and effect, for example. But it was not always so: Many happenings in the natural world

seemed very random and unfathomably complex to the ancients and to early scientists. Research that

adhered carefully to the scientific method brought the predictability upon which so much progress has been

built. Even when our most advanced theories have convinced scientists that the world is not deterministic, at

least the phenomena are predictably random.

Infectious diseases, for example, at one point just seemed to strike at random. People didn’t understand

what caused them. Who survived and who did not seemed unpredictable. Although the outcome seemed

random, however, the process that led to the results was not random— it just was not sufficiently understood.

With many cancers today, as in the venture capitalists’ world, patients’ probabilities for survival can only be

articulated in percentages. This is not because the outcomes are unpredictable, however. We just do not yet

understand the process.

[16]

Peter Senge calls theories mental models (see Peter Senge, The Fifth Discipline [New York: Bantam

Doubleday Dell, 1990]). We considered using the term model in this book, but opted instead to use the term

theory. We have done this to be provocative, to inspire practitioners to value something that is indeed of value.

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[17]

A full description of the process of theory building and of the ways in which business writers and

academics ignore and violate the fundamental principles of this process is available in a paper that is

presently under review, he Process of Theory Building,?by Clayton Christensen, Paul Carlile, and David

Sundahl. Paper or electronic copies are available from Professor Christensen’s office, cchristensen@hbs.edu.

The scholars we have relied upon in synthesizing the model of theory building presented in this paper (and

only very briefly summarized in this book) are, in alphabetical order, E. H. Carr, What Is History? (New York:

Vintage Books, 1961); K. M. Eisenhardt, uilding Theories from Case Study Research,?Academy of

Management Review 14, no. 4 (1989): 532–550; B. Glaser and A. Straus, The Discovery of Grounded Theory:

Strategies of Qualitative Research (London: Wiedenfeld and Nicholson, 1967); A. Kaplan, The Conduct of

Inquiry: Methodology for Behavioral Research (Scranton, PA: Chandler, 1964); R. Kaplan, he Role for

Empirical Research in Management Accounting,?Accounting, Organizations and Society 4, no. 5 (1986): 429

–452; T. Kuhn, The Structure of Scientific Revolutions (Chicago: University of Chicago Press, 1962); M.

Poole and A. Van de Ven, sing Paradox to Build Management and Organization Theories,?Academy of

Management Review 14, no. 4 (1989): 562–578; K. Popper, The Logic of Scientific Discovery (New York:

Basic Books, 1959); F. Roethlisberger, The Elusive Phenomena (Boston: Harvard Business School Division

of Research, 1977); Arthur Stinchcombe, he Logic of Scientific Inference,?chapter 2 in Constructing Social

Theories (New York: Harcourt, Brace & World, 1968); Andrew Van de Ven, rofessional Science for a

Professional School,?in Break ing the Code of Change, eds. Michael Beer and Nitin Nohria (Boston: Harvard

Business School Press, 2000); Karl E. Weick, heory Construction as Disciplined Imagination,?Academy

of Management Review 14, no. 4, (1989): 516–531; and R. Yin, Case Study Research (Beverly Hills, CA:

Sage Publications, 1984).

[18]

What we are saying is that the success of a theory should be measured by the accuracy with which it can

predict outcomes across the entire range of situations in which managers find themselves. Consequently, we

are not seeking ruth?in any absolute, Platonic sense; our standard is practicality and usefulness. If we

enable managers to achieve the results they seek, then we will have been successful. Measuring the

success of theories based on their usefulness is a respected tradition in the philosophy of science,

articulated most fully in the school of logical positivism. For example, see R. Carnap, Empiricism, Semantics

and Ontology (Chicago: University of Chicago Press, 1956); W. V. O. Quine, Two Dogmas of Empiricism

(Cambridge, MA: Harvard University Press, 1961); and W. V. O. Quine, Epistemology Naturalized. (New

York: Columbia University Press, 1969).

[19]

This is a serious deficiency of much management research. Econometricians call this practice ampling

on the dependent variable.?Many writers, and many who think of themselves as serious academics, are so

eager to prove the worth of their theories that they studiously avoid the discovery of anomalies. In case study

research, this is done by carefully selecting examples that support the theory. In more formal academic

research, it is done by calling points of data that don’t fit the model utliers?and finding a justification for

excluding them from the statistical analysis. Both practices seriously limit the usefulness of what is written. It

actually is the discovery of phenomena that the existing theory cannot explain that enables researchers to

build better theory that is built upon a better classification scheme. We need to do anomaly-seek ing

research, not anomaly-avoiding research.

We have urged doctoral students who are seeking potentially productive research questions for their thesis

research to simply ask when a ad?theory won’t work— for example, hen is process reengineering a bad

idea??Or, ight you ever want to outsource something that is your core competence, and do internally

something that is not your core competence?

Asking questions like this almost always improves the validity of the original theory. This opportunity to

improve our understanding often exists even for very well done, highly regarded pieces of research. For

example, an important conclusion in Jim Collins’s extraordinary book From Good to Great (New York:

HarperBusiness, 2001) is that the executives of these successful companies weren’t charismatic, flashy men

and women. They were humble people who respected the opinions of others. A good opportunity to extend

the validity of Collins’s research is to ask a question such as, re there circumstances in which you

actually don’t want a humble, noncharismatic CEO??We suspect that there are— and defining the different

circumstances in which charisma and humility are virtues and vices could do a great service to boards of

directors.

[20]

We thank Matthew Christensen of the Boston Consulting Group for suggesting this illustration from the

world of aviation as a way of explaining how getting the categories right is the foundation for bringing

predictability to an endeavor. Note how important it was for researchers to discover the circumstances in

which the mechanisms of lift and stabilization did not result in successful flight. It was the very search for

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