Open Innovation is a topic of scientific interest and economic significance to academic researchers, business practitioners and government policy makers. In 2003, Henry Chesbrough described stories from IBM, Intel and Lucent as innovation case studies of managing innovation processes in technology (Chesbrough 2003). The changes to progress open innovation were furthered in 2007, beyond innovation processes to focus on business models, with Air Products, IBM and Procter & Gamble as extended cases (Chesbrough 2007), This research opened up managerial thinkers to the insight that external ideas and technologies can be brought into a company, and that internal knowledge might flow out for alternative uses in other organizations.
Open Innovation Learning is a new framework that aims to build on case studies, but not get stuck in the past. Studying the histories of organizations that have been successful in innovating gives grounding on managerial actions that have worked (or not worked). With rapid changes in technologies and societies, however, looking backwards is not sufficient. Organizations and individuals mutually engage in learning. The learning is not only about the external contexts, but in the relationships between products, services and people within the organization.
In this book, David Ing deepens the history of case studies of innovating over a decade at IBM. Across parallel timelines, three descriptive theories of open innovation learning are developed. Additionally, normative theory is proposed for managers seeing the world of 2020 just over the horizon. In this brief foreword, I will try to cover a bit of the past, present, and future evoked by reading this complex, but important book.
First, a bit about the past. Before there was open innovation, there was Intellectual Property (IP) to be created privately by a sponsoring company, legally protected against infringement, and monetized as a reward for prior investments. The early 20th century largely saw the surge of technological and economic change associated with oil, automobiles and mass production. The 1970s saw the beginnings of a surge with the rise of the computer and the Internet (Perez 2002). As we enter what is arguably just the third century of technology-driven innovation firms (e.g. in industries of transportation, chemicals, communications, and information), organizations are evolving their organizational IP strategies. The low barriers to entry and capacity for rapid change in software innovation feed a new fitness function reshaping businesses for competing in a multi-vendor, multi-platform world. Over the past 10 years, there has been incredible growth in the open source community. One report counts 24 million developers working across 67 million repositories (GitHub Inc. 2017). In this book, researchers may gain insight into the phenomenon, with a source of concrete data (as in-depth case studies) in addition to emerging theories (both descriptive and normative) to be further developed.
The case studies in the book are drawn from the author’s experiences as an enterprise systems architect at IBM. At the dawn of e-business around 1997, IBM discovered that software products built in-house were not keeping pace with competitors. The IBM investments with the Apache web server in 1998 and the Linux operating system in 2000 proved that supporting carefully selected open standards could disrupt marketplace contests with vendors pushing proprietary offerings. Since those days, IBM has learned there are even more advantages to this strategy. This book traces that story.
Before 2000, from an IP perspective, two types of organizations dominated: (i) PSo (Private Sourcing only) businesses, primarily for-profit enterprises seeking to maximize value capture; and (ii) OSo (Open Sourcing only) not-for-profit organizations, primarily ecosystems seeking to maximize adoption and growth. Over the next decade, a third way emerged as a hybrid, described as OSwPS (Open Sourcing while Private Sourcing). This phenomenon is recounted in great detail in this book, using seven case studies that highlight IBM as a lead originator of OSwPS from 2001 to 2011.
During that decade, David Ing had a front row seat to observe, study, and participate in these changes at IBM and in the industry. During this same period, David and I worked together on IBM's SSME (Service Science Management and Engineering) initiative, an emerging academic discipline studying service systems as a type of complex socio-technical system. Service systems are noteworthy, in part, due to their wide range of value co-creation mechanisms, as well as their potential for entities to co-elevate their capabilities for collaborating and competing in a rapidly evolving ecology of other service system entities. Service-Dominant logic (S-D logic) refers to these entities as resource integrators, engaging in service for service exchange, and giving rise to markets, the focus of the study of marketing and economics (Vargo and Lusch 2017).
From a service science and S-D logic perspective, PSo, OSo, and OSwPS organizations can be viewed as three types of service system entities or resource integrators.
The first major contribution of this book is its presentations of three descriptive theories of OSwPS organizations. The three descriptive theories are: (i) quality-generating sequencing from an architectural problem-seeking paradigm, (ii) affordance wayfaring from an inhabiting disclosive spaces paradigm, and (ii) anticipatory appreciating from a governing subworlds paradigm. Grounding these descriptive theories is the business imperative to create high-quality artifacts (i.e. code) by engaging highly skilled talent (people) across multiple organizations and communities (i.e. ecosystem), and maintaining highly ethical conduct while competing for customers, employees, partners and stakeholders (i.e. incentives for collaborators).
The second major contribution of this book is its presentation of seven case studies drawn from IBM's history as the company transformed more fully into OSwPS from PSo. The case studies are: (i) integrating-development, (ii) microblogging, (iii) blogging, (iv) wikiing, (v) podcasting, (vi) mashing-up, and (vii) co-authoring. Again, grounding these case studies in business imperatives shows a path whereby people (i.e. highly skilled talent) create code while organizations ethically compete for collaborators across a dynamic, innovative ecosystem. After absorbing these case studies and descriptive theories, one is left wondering how will the range of potential case studies continue to evolve in the future?
However, before looking into the future of open innovation learning, next, a bit about the present. Readers interested in IBM's continuing investment in technology communities and projects, as described by IBM itself, can browse “IBM's approach to open technology”. Successful collaboration for state-of-the-art open source amongst highly competitive businesses relies on working open governance:
One thing IBM has learned through all of this is that those communities that strive for inclusiveness and open governance tend to attract the largest ecosystems and most expansive markets. However, not all open source is created equal, and not all communities thrive. There is a broad range of open source, and much of it is not truly open... The reality is that open technology projects managed under open governance — true open governance as we have with organizations such as Apache, Eclipse, OpenStack, Mozilla, and Linux — are demonstrably more successful (by an order of magnitude), have a longer life, and are less risky than those projects that are controlled by a single vendor, or are more restrictive in their governance (Moore and Ferris 2016).
Now we are ready, finally, for a bit about the future. As I write this foreword, I am leading IBM's Cognitive Opentech Group (COG). Cognitive Opentech can be seen as open innovation learning in action. The rise of AI (Artificial Intelligence) and IA (Intelligence Augmentation) will hopefully lead to not just smarter, but wiser service systems (Spohrer et al. 2017). The legal IP frameworks are now well-established for licensing. However, some companies have been slower to adopt standard practices than others.
AI is acting as a forcing function for companies to work together like never before, to realize the benefits and mitigate the risks of AI. Since 2015, OpenAI was founded as a non-profit research company promoting paths to safe artificial general intelligence, and Partnership on AI was formed as an industry consortium to establish best practices that will benefit people and society.
From the Kubernetes system for containerized applications to the TensorFlow library for dataflow programming, there is a growing realization of the benefits of open source. SystemML is a scalable machine learning system originating from IBM Research that was pledged into the Apache Foundation, now a top-level project with open governance. MxNet is a scalable deep learning framework backed by Microsoft and Amazon that is also on the pathway towards open governance. The ONNX Open Neural Network Exchange sees Microsoft and Facebook collaborating with other industry partners. The Acumos Project, a directory for finding and sharing apps and microservices, has AT&T and Tech Mahindra partnering with the Linux Foundation to create an open marketplace for AI models.
This thriving activity is still just early days for commercial AI. The growth of open AI Leaderboards to measure AI progress shows that open innovation learning is at play with open competition platforms such as Kaggle (acquired by Google in 2017). The opportunities for open source in platform thinking and a world ruled by algorithms are further explored in WTF? What's the future and why it is up to us (O’Reilly 2017).
In sum, this book provides researchers who study open source phenomena a feast of data and theories based on the past, while pointing the way into the future with the concept of open innovation learning. It remains for other scholars to add additional data and case studies from a wider range of organizations, as well as refinements or replacement of the theories presented herein. Nevertheless, this book provides contribution to the field and grows our collective knowledge of open source phenomena, as well as providing a solid foundation for future studies of open innovation learning. From a service science (SSME) perspective, this book illustrates the evolving ecology of service system entities, including their value co-creation mechanisms and capability co-elevation mechanisms around the shared technology resource of open source code and shared talent resource of multi-role skilled people.
Jim Spohrer
San Jose, California, USA
November 2017
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