DMCA and Copyright : The book is not hosted on our servers, to remove the file please contact the source url. If you see a Google Drive link instead of source url, means that the file witch you will get after approval is just a summary of original book or the file has been already removed. Loved each and every part of this book. I will definitely recommend this book to non fiction, history lovers. Want more? Advanced embedding details, examples, and help! Includes bibliographical references and index The growth imperative -- How can we beat our most powerful competitors?
The Innovator's Solution shows how companies get to the side of this dilemma, creating disruptions rather than being destroyed by them. They identify the forces that cause managers to make bad decisions as they package and shape new ideas - and offer new frameworks to help managers create the right conditions, at the right time, for a disruption to succeed.
Comment Removing Scanfee from Billable Books scanned before June which appear to have manually set scanfees Donor bostonpubliclibrary Edition [Nachdr. Add a review Your Rating: Your Comment:. Hot Cry of the Peacock by V. Christensen by V. The Lucky One by Nicholas Sparks. Le Porte-Bonheur by Nicholas Sparks. 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.
But academics, consultants, and managers routinely dispense and accept remedies to management problems in this manner. 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 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.
But in the late s we read that non-integration explained the triumph of outsourcing titans such as Cisco and Dell. 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. Early researchers observed strong correlations between being able to fly and having feathers and wings.
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.
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. 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 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. 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 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. 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. To succeed predictably, disruptors must be good theorists. As they shape their growth business to be disruptive, they must align every critical process and decision to fit the disruptive circumstance. Because building successful growth businesses is such a vast topic, this book focuses on nine of the most important decisions that all managers must make in creating growth—decisions that represent key actions that drive success inside the black box of innovation.
Each chapter offers a specific theory that managers can use to make one of these decisions in a way that greatly improves their probability of success.
Some of this theory has emerged from our own studies, but we are indebted to many other scholars for much of what follows.
Those whose work we draw upon have contributed to improving the predictability of business building because their assertions of causality have been built upon circumstance-based categories. It is because of their careful work that we believe that managers can begin using these theories explicitly as they make these decisions, trusting that their predictions will be applicable and reliable, given the circumstances that they are in.
The following list summarizes the questions we address. Chapter 2: How can we beat our most powerful competitors? What strategies will result in the competitors killing us, and what courses of action could actually give us the upper hand? Chapter 3: What products should we develop? Which improvements over previous products will customers enthusiastically reward with premium prices, and which will they greet with indifference?
Chapter 4: Which initial customers will constitute the most viable foundation upon which to build a successful business? Chapter 5: Which activities required to design, produce, sell, and distribute our product should our company do internally, and which should we rely upon our partners and suppliers to provide?
Chapter 6: How can we be sure that we maintain strong competitive advantages that yield attractive profits? How can we tell when commoditization is going to occur, and what can we do to keep earning attractive returns?
Chapter 7: What is the best organizational structure for this venture? What organizational unit s and which managers should contribute to and be responsible for its success? Chapter 8: How do we get the details of a winning strategy right? When is flexibility important, and when will flexibility cause us to fail?
Chapter 9: Whose investment capital will help us succeed, and whose capital might be the kiss of death? What sources of money will help us most at different stages of our development?
Chapter What role should the CEO play in sustaining the growth of the business? When should CEOs keep their hands off the new business, and when should they become involved? The issues that we tackle in these chapters are critical, but they cannot constitute an exhaustive list of the questions that should be relevant to launching a new-growth business.
Notes 1. Although we have not performed a true meta-analysis, there are four recently published studies that seem to converge on this estimate that roughly one company in ten succeeds at sustaining growth. Chris Zook and James Allen found in their study Profit from the Core Boston: Harvard Business School Press that only 13 percent of their sample of 1, companies were able to grow consistently over a ten-year period.
They learned that only , or about 16 percent of these firms, were able merely to survive this time frame, and concluded that the perennially outperforming company is a chimera, something that has never existed at all.
Jim Collins also published his Good to Great New York: HarperBusiness in , in which he examined a universe of 1, companies over thirty years — Collins found only , or about 9 percent, that had managed to outperform equity market averages for a decade or more. The studies all support our assertion that a 10 percent probability of succeeding in a quest for sustained growth is, if anything, a generous estimate.
Although when a deal actually closes, a definitive value can be fixed, the implied value of the transaction at the time a deal is announced can be useful: It signals what the relevant parties were willing to pay and accept at a point in time. Where possible, we have used the value of the deals at announcement, rather than upon closing. Between and , however, its stock price deteriorated again, reflecting the dearth of growth prospects.
Professor Jensen also delivered this paper as his presidential address to the American Finance Association. Interestingly, many of the firms that Jensen cites as having productively reaped growth from their investments were disruptive innovators—a key concept in this book.
Our assertion here is that whatever the track record of free market economies in generating growth at the macro level, the track record of individual firms is quite poor. It is the performance of firms within a competitive market to which we hope to contribute.
Empirical analysis suggests that the market does not expect any company to grow, or even survive, forever. It therefore seems to incorporate into current prices a foreseen decline in growth rates from current levels and the eventual dissolution of the firm.
This is the reason for the importance of terminal values in most valuation models. This fade period is estimated using regression analysis, and estimates vary widely. So, strictly speaking, if a company is expected to grow at 5 percent with a fade period of forty years, and five years into that forty-year period it is still growing at 5 percent, the stock price would rise at rates that generated economic returns for shareholders, because the forty-year fade period would start over. However, because this qualification applies to companies growing at 5 percent as well as those growing at 25 percent, it does not change the point we wish to make; that is, that the market is a harsh taskmaster, and merely meeting expectations does not generate meaningful reward.
On average over their long histories, of course, faster-growing firms yield higher returns. However, the faster-growing firm will have produced higher returns than the slower- growing firm only for investors in the past. If markets discount efficiently, then the investors who reap above-average returns are those who were fortunate enough to have bought shares in the past when the future growth rate had not been fully discounted into the price of the stock.
Those who bought when the future growth potential already had been discounted into the share price would not receive an above-market return. An excellent reference for this argument can be found in Alfred Rappaport and Michael J.
There is no significance to that particular date: It is simply the time when the analysis was done. Percent future begins with the total market value debt plus equity less that portion attributed to the present value of existing assets and investments and divides this by the total market value of debt and equity.
In the text we have focused only on the pressure that equity markets impose on companies to grow, but there are many other sources of intense pressure. First, when a company is growing, there are increased opportunities for employees to be promoted into new management positions that are opening up above them.
Hence, the potential for growth in managerial responsibility and capability is much greater in a growing firm than in a stagnant one. When growth slows, managers sense that their possibilities for advancement will be constrained not by their personal talent and performance, but rather by how many years must pass before the more senior managers above them will retire.
Investment in new technologies also becomes difficult. When a growing firm runs out of capacity and must build a new plant or store, it is easy to employ the latest technology. When a company has stopped growing and has excess manufacturing capacity, proposals to invest in new technology typically do not fare well, since the full capital cost and the average manufacturing cost of producing with the new technology are compared against the marginal cost of producing in a fully depreciated plant.
As a result, growing firms typically have a technology edge over slow-growth competitors. But that advantage is not rooted so much in the visionary wisdom of the managers as it is in the difference in the circumstances of growth versus no growth. Detailed support for this estimate is provided in note 1.
Irwin, In this view, the key to successful innovation lies in choosing the right flowers to tend—and that decision must rely on complex intuitive feelings, calibrated by experience. Advocates of this approach urge corporate executives not to punish failures because it is only through repeated attempts that successful new businesses will emerge.
Others draw on analogies with biological evolution, where mutations arise in what appear to be random ways. We are not critical of these books. They can be very helpful, given the present state of understanding, because if the processes that create innovations were indeed random, then a context within which managers could accelerate the creation and testing of ideas would indeed help. But if the process is not intrinsically random, as we assert, then addressing only the context is treating the symptom, not the source of the problem.
But what guidance does this policy give to a bench engineer at 3M? She is also told that whatever she comes up with will be subject first to internal market selection pressures, then external market selection pressures. All this is helpful information. But none of it helps that engineer create a new idea, or decide which of the several ideas she might create are worth pursuing further. This plight generalizes to managers and executives at all levels in an organization.
From bench engineer to middle manager to business unit head to CEO, it is not enough to occupy oneself only with creating a context for innovation that sorts the fruits of that context.
Ultimately, every manager must create something of substance, and the success of that creation lies in the decisions managers must make. Ultimately, innovators must judge what they will work on and how they will do it—and what they should consider when making those decisions is what is in the black box. The acceptance of randomness in innovation, then, is not a stepping-stone on the way to greater understanding; it is a barrier.
Gary Hamel was one of the first scholars of this problem to raise with Professor Christensen the possibility that the management of innovation actually has the potential to yield predictable results. We express our thanks to him for his helpful thoughts. We owe a deep intellectual debt to them. See Joseph L. Christensen and Scott D. 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. 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. 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. 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. This is not because the outcomes are unpredictable, however. We just do not yet understand the process.
We have done this to be provocative, to inspire practitioners to value something that is indeed of value. 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. Carr, What Is History? New York: Vintage Books, ; K.
Glaser and A. Poole and A. 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. 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. Quine, Epistemology Naturalized. New York: Columbia University Press, This is a serious deficiency of much management research. In case study research, this is done by carefully selecting examples that support the theory.
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. This opportunity to improve our understanding often exists even for very well done, highly regarded pieces of research.
They were humble people who respected the opinions of others. 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 failures that made success consistently possible.
Unfortunately, many of those engaged in management research seem anxious not to spotlight instances their theory did not accurately predict. They engage in anomaly-avoiding, rather than anomaly-seeking, research and as a result contribute to the perpetuation of unpredictability. Hence, we lay much responsibility for the perceived unpredictability of business building at the feet of the very people whose business it is to study and write about these problems.
We may, on occasion, succumb to the same problem. We can state that in developing and refining the theories summarized in this book, we have truly sought to discover exceptions or anomalies that the theory would not have predicted; in so doing, we have improved the theories considerably. But anomalies remain. Where we are aware of these, we have tried to note them in the text or notes of this book. If any of our readers are familiar with anomalies that these theories cannot yet explain, we invite them to teach us about them, so that together we can work to improve the predictability of business building further.
In studies of how companies deal with technological change, for example, early researchers suggested attribute-based categories such as incremental versus radical change and product versus process change. Each categorization supported a theory, based on correlation, about how entrant and established companies were likely to be affected by the change, and each represented an improvement in predictive power over earlier categorization schemes.
At this stage of the process there rarely is a best-by-consensus theory, because there are so many attributes of the phenomena. Scholars of this process have broadly observed that this confusion is an important but unavoidable stage in building theory. Kuhn chronicles at length the energies expended by advocates of various competing theories at this stage, prior to the advent of a paradigm. In addition, one of the most influential handbooks for management and social science research was written by Barney G.
Glaser and Anselm L. Managers need to know if a theory applies in their situation, if they are to trust it. A very useful book on this topic is Robert K. There is no other way to gauge where theory applies and where it does not. The concern that readers of the disk drive study raised, of course, was whether the theory applied to other industries as well. Readers continued to ask whether the theory applied to chemicals, to database software, and so on. Applying any theory to industry after industry cannot prove its applicability because it will always leave managers wondering if there is something different about their current circumstances that renders the theory untrust-worthy.
A theory can confidently be employed in prediction only when the categories that define its contingencies are clear. This is all well and good. An illustration of how important it is to get the categories right can be seen in the fascinating juxtaposition of two recent, solidly researched books by very smart students of management and competition that make compelling cases for diametrically opposite solutions to a problem. Each team of researchers addresses the same underlying problem—the challenge of delivering persistent, profitable growth.
At the same time, another well-executed study, Profit from the Core Boston: Harvard Business School Press, , by Bain consultants Chris Zook and James Allen, drew upon the same phenomenological evidence—that only a tiny minority of companies are able to sustain above- market returns for a significant time.
But their book encourages companies to focus on and improve their established businesses rather than attempt to anticipate or even respond to the vagaries of equity investors by seeking to create new growth in less-related markets.
0コメント