Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

This article introduces the concept of the capability lifecycle (CLC), which articulates general patterns and paths in the evolution of organizational capabilities over time. The capability lifecycle provides a structure for a more comprehensive approach to dynamic resource-based theory. The analysis incorporates the founding, development, and maturity of capabilities in a manner that helps to explain the sources of heterogeneity in organizational capabilities. In addition, the analysis includes the "branching" of an original capability into several possible altered forms.

The Dynamic Resource-Based View: Capability Lifecycles

This article introduces the concept of the capability lifecycle (CLC), which articulates general patterns and paths in the evolution of organizational capabilities over time. The capability lifecycle provides a structure for a more comprehensive approach to dynamic resource-based theory. The analysis incorporates the founding, development, and maturity of capabilities in a manner that helps to explain the sources of heterogeneity in organizational capabilities. In addition, the analysis includes the ‘branching’ of an original capability into several possible altered forms. Copyright © 2003 John Wiley & Sons, Ltd.

The dynamic resource-based view: capability lifecycles

We address current criticisms of the RBV (oversight of dynamism, environmental contingencies, and managers' role) by linking value creation in dynamic environmental contexts to the management of firm resources. Components of the resource management model include structuring the resource portfolio; bundling resources to build capabilities; and leveraging capabilities to provide value to customers, gain a competitive advantage, and create wealth for owners. Propositions linking resource management and value creation are offered to shape future research.

https://www.jstor.org/stable/pdf/20159292.pdf

Managing Firm Resources in Dynamic Environments to Create Value: Looking Inside the Black Box

What makes organizations so similar? We contend that the engine of rationalization and bureaucratization has moved from the competitive marketplace to the state and the professions. Once a set of organizations emerges as a field, a paradox arises: rational actors make their organizations increasingly similar as they try to change them. We describe three isomorphic processes-coercive, mimetic, and normative—leading to this outcome. We then specify hypotheses about the impact of resource centralization and dependency, goal ambiguity and technical uncertainty, and professionalization and structuration on isomorphic change. Finally, we suggest implications for theories of organizations and social change.

The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields (Chinese Translation)

The success of an innovating firm often depends on the efforts of other innovators in its environment. How do the challenges faced by external innovators affect the focal firm's outcomes? To address this question we first characterize the external environment according to the structure of interdependence. We follow the flow of inputs and outputs in the ecosystem to distinguish between upstream components that are bundled by the focal firm, and downstream complements that are bundled by the firm's customers. We argue that the effect of external innovation challenges depends not only on their magnitude, but also on their location in the ecosystem relative to the focal firm - whereas greater innovation challenges in components enhances the benefits that accrue to technology leaders, greater innovation challenges in complements erodes these benefits. We further argue that the effectiveness of vertical integration as a strategy to manage ecosystem interdependence increases over the course of the technology life cycle. We explore these arguments in the context of the global semiconductor lithography industry from its emergence in 1962 to 2005 across nine distinct technology generations. We find strong support for our arguments.

/pdf/value-creation-in-innovation-ecosystems-how-the-structure-of-gp7v1ema64.pdf

Value Creation in Innovation Ecosystems: How the Structure of Technological Interdependence Affects Firm Performance in New Technology Generations

The success of an innovating firm often depends on the efforts of other innovators in its environment. How do the challenges faced by external innovators affect the focal firm's outcomes? To address this question we first characterize the external environment according to the structure of interdependence. We follow the flow of inputs and outputs in the ecosystem to distinguish between upstream components that are bundled by the focal firm, and downstream complements that are bundled by the firm's customers. We hypothesize that the effects of external innovation challenges depend not only on their magnitude, but also on their location in the ecosystem relative to the focal firm. We identify a key asymmetry that results from the location of challenges relative to a focal firm—greater upstream innovation challenges in components enhance the benefits that accrue to technology leaders, while greater downstream innovation challenges in complements erode these benefits. We further propose that the effectiveness of vertical integration as a strategy to manage ecosystem interdependence increases over the course of the technology life cycle. We explore these arguments in the context of the global semiconductor lithography equipment industry from its emergence in 1962 to 2005 across nine distinct technology generations. We find strong empirical support for our framework. Copyright © 2009 John Wiley & Sons, Ltd.

/pdf/value-creation-in-innovation-ecosystems-how-the-structure-of-2oo2sqb3g1.pdf

Value creation in innovation ecosystems: how the structure of technological interdependence affects firm performance in new technology generations

Corporate effects in variance decomposition capture heterogeneity of business performance derived from factors internal to firms at the corporate level. Most estimates of corporate effects do not include effects associated with fluctuations in returns over time, except insofar as the fluctuations affect the average corporate return for the time period in question. Exclusion of the time-varying dimension of the corporate effect makes it difficult to fully understand the effect of corporate strategy and the actions of corporate managers, particularly in response to a changing environment. The evidence in this article shows that within a single industry, where managers face the same external environment, time-varying corporate effects associated with corporate level managerial decisions are statistically significant. We introduce the concept of dynamic managerial capabilities to underpin the finding of heterogeneity in managerial decisions and firm performance in the face of changing external conditions. Copyright © 2003 John Wiley & Sons, Ltd.

Corporate effects and dynamic managerial capabilities

High-definition televisions should, by now, be a huge success. Philips, Sony, and Thompson invested billions of dollars to develop TV sets with astonishing picture quality. From a technology perspective, they've succeeded: Console manufacturers have been ready for the mass market since the early 1990s. Yet the category has been an unmitigated failure, not because of deficiencies, but because critical complements such as studio production equipment were not developed or adopted in time. Under-performing complements have left console producers in the position of offering a Ferrari in a world without gasoline or highways--an admirable engineering feat, but not one that creates value for customers. The HDTV story exemplifies the promise and peril of innovation ecosystems--the collaborative arrangements through which firms combine their individual offers into a coherent, customer-facing solution. When they work, innovation ecosystems allow companies to create value that no one firm could have created alone. The benefits of these systems are real. But for many organizations the attempt at ecosystem innovation has been a costly failure. This is because, along with new opportunities, innovation ecosystems also present a new set of risks that can brutally derail a firm's best efforts. Innovation ecosystems are characterized by three fundamental types of risk: initiative risks--the familiar uncertainties of managing a project; interdependence risks--the uncertainties of coordinating with complementary innovators; and integration risks--the uncertainties presented by the adoption process across the value chain. Firms that assess ecosystem risks holistically and systematically will be able to establish more realistic expectations, develop a more refined set of environmental contingencies, and arrive at a more robust innovation strategy. Collectively, these actions will lead to more effective implementation and more profitable innovation.

Match your innovation strategy to your innovation ecosystem

https://journals.sagepub.com/doi/pdf/10.1177/0149206316678451

Ron Adner

Papers

Value Creation in Innovation Ecosystems: How the Structure of Technological Interdependence Affects Firm Performance in New Technology Generations

Value creation in innovation ecosystems: how the structure of technological interdependence affects firm performance in new technology generations

Corporate effects and dynamic managerial capabilities

Match your innovation strategy to your innovation ecosystem

Ecosystem as Structure: An Actionable Construct for Strategy