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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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