Open Innovation Results: Going Beyond the Hype, and Getting Down to Business
Authors: Henry Chesbrough, Henry Chesbrough
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Overview
This book examines how to achieve tangible results from Open Innovation, moving beyond the hype and providing practical guidance for organizations and societies. I address the “Exponential Paradox”—the disconnect between rapid technological advancement and stagnating economic productivity—and argue that the key lies in effective innovation management, focusing not only on generating new ideas but also on their dissemination and absorption. I redefine Open Innovation as a distributed process based on managed knowledge flows across organizational boundaries, and examine its implications for intellectual property, business models, and organizational structures. The book explores the “back end” of innovation, where innovations are transferred and commercialized within organizations, highlighting common challenges like organizational resistance, funding gaps, and the importance of leadership support. It examines how Lean Startup principles, emphasizing customer development and iterative progress, can be adapted and applied within large corporations to drive new business creation. The book examines various engagement models between corporations and startups, ranging from equity-based approaches to more scalable, influence-based models. Furthermore, the book investigates Open Innovation’s application in diverse contexts, including smart cities, rural villages, and the unique innovation landscape of modern China. Throughout, I emphasize the importance of adapting Open Innovation practices to specific organizational and societal contexts, outlining best practices, learning from failures like Quirky, and ultimately, providing readers with practical guidance and a deeper understanding of how Open Innovation can deliver real results. The book targets innovation managers, corporate executives, policymakers, and researchers seeking to understand and apply Open Innovation effectively. It contributes to the ongoing debate about the role of innovation in economic growth and societal progress, offering insights into managing innovation in an increasingly complex and rapidly changing world.
Book Outline
1. The Exponential Paradox
Despite the rise of “exponential technologies,” economic productivity and personal incomes are stagnating. This “Exponential Paradox” challenges the assumption that technological advancements automatically translate into economic prosperity. The root of this problem lies in how we manage and invest in innovation, specifically neglecting the dissemination and absorption of innovations.
Key concept: The “Exponential Paradox” describes the disconnect between rapid technological advancement, which grows exponentially, and lagging economic productivity and income growth.
2. Open Innovation in the Twenty-First Century
Open Innovation is a paradigm shift from closed innovation models. It involves leveraging both external and internal knowledge flows to accelerate internal innovation and expand the application of internal knowledge in external markets. This model emphasizes the importance of business models in converting technology into commercial value and challenges traditional views on intellectual property management.
Key concept: Open Innovation is a distributed innovation process based on purposively managed knowledge flows across organizational boundaries, using pecuniary and non-pecuniary mechanisms in line with the organization’s business model.
3. From Open Science to Open Innovation
Open science fosters rapid knowledge dissemination but often faces challenges in commercialization. Open Innovation addresses this gap by incorporating business model development, aligning incentives for application, and strategically managing intellectual property.
Key concept: Open Innovation can act as a bridge to connect the fruits of open science with effective commercialization. It requires the development of a business model, which enables value creation and capture within the innovation system.
4. The Back End of Open Innovation
Effective Open Innovation requires careful management of the “back end” of the process, where innovations are transferred to business units for commercialization. This stage often faces bottlenecks due to limited resources, internal resistance, funding gaps, and lack of senior management support.
Key concept: The “back end” of innovation involves transferring innovation from the front end to the business units, requiring careful attention to organizational challenges and knowledge transfer.
5. Lean Startup and Open Innovation
Lean Startup principles, focusing on minimizing waste and maximizing customer feedback, can be effectively applied within large organizations. Lean Startup practices within corporations require adaptation due to existing business models and processes, which are absent in startups.
Key concept: Lean Startup’s core insight is that most startups fail due to a lack of customer acceptance, not because of poor product development.
6. Engaging with Startups to Enhance Corporate Innovation
Large corporations and startups can achieve mutual benefits through collaboration, but careful consideration must be given to the engagement model. Traditional equity-based models, such as Corporate Venture Capital, provide control but may not scale well. Lighter-weight models based on influence, such as startup programs, can scale more easily and facilitate faster collaboration.
Key concept: Corporations must select the engagement model with startups that best aligns with their strategic goals, balancing control versus influence and managing cultural differences.
7. Open Innovation Results in Smart Cities and Smart Villages
Smart Cities initiatives, while conceptually promising, have had limited success due to insufficient attention to dissemination and absorption. The focus has been on generating new technological possibilities, often overlooking the need to ensure widespread adoption and integration within the city’s existing systems. The Smart Village initiative, in contrast, demonstrates a more balanced approach.
Key concept: Smart Cities initiatives have faced challenges in demonstrating tangible benefits due to a focus on technology generation rather than dissemination and absorption.
8. Open Innovation Best Practices
Successful Open Innovation is not merely about adopting certain practices, but about embracing a mindset of external collaboration and continuous improvement. Companies like P&G, GE, Enel, and Bayer have demonstrated effective Open Innovation strategies, although even successful programs can falter with changes in leadership or competitive dynamics.
Key concept: Open Innovation is not just a set of practices or tactics. It stems from a mindset and a belief.
9. Open Innovation with Chinese Characteristics
China’s approach to Open Innovation is shaped by the unique context of its political system and economic development stage. The Chinese Communist Party plays a significant role in guiding industrial development, creating both opportunities and challenges for Open Innovation practices.
Key concept: Open Innovation with Chinese characteristics represents a unique blend of policy and market forces, influenced by the role of the CCP.
Essential Questions
1. What is the ‘Exponential Paradox’, and what are its underlying causes?
The ‘Exponential Paradox’ highlights the disconnect between the rapid pace of technological development and the slow growth in productivity and incomes. This is because while there’s been heavy investment in generating new innovations, insufficient attention has been paid to disseminating them throughout society and enabling its absorption into business and operations. This shortfall in dissemination and absorption is due to insufficient investment in the knowledge infrastructure of the society, especially in the capabilities of its citizens, its universities, its research labs and its supporting institutions. To overcome this paradox, we need to invest more effectively in disseminating the knowledge widely, and enhancing the absorption of that knowledge in many more organizations and for many more citizens. Without addressing the absorption bottleneck, our societies will not achieve the full potential of the new technologies being created.
2. How does Open Innovation differ from traditional, closed innovation models, and what are its key characteristics?
Open Innovation is a distributed innovation process where firms use both external and internal knowledge, ideas, and paths to market. It differs from closed innovation, which primarily relies on internal R&D. Key aspects of Open Innovation include: 1) recognizing the abundance of external knowledge, 2) utilizing business models to translate technology into value, 3) managing inflows and outflows of knowledge, and 4) strategically leveraging IP. It involves both “outside-in” and “inside-out” processes, where external knowledge is brought into the firm, and internal knowledge is made available to others, respectively. Successful Open Innovation requires overcoming challenges such as Not-Invented-Here syndrome, organizational silos, and funding bottlenecks.
3. What are the critical challenges associated with the ‘back end’ of Open Innovation, and how can these be addressed?
The “back end” of Open Innovation focuses on transferring innovations from R&D or innovation units to the business units responsible for commercialization. This stage is crucial for realizing business results but often encounters significant hurdles. Common challenges include: 1) organizational resistance from employees threatened by external knowledge or fearing job displacement; 2) funding gaps, as innovative projects often struggle to secure ongoing funding from business units; and 3) lack of senior management support to champion innovative projects and navigate internal politics. Successfully navigating the back end requires strategies like creating dedicated funding for project transfers, assigning internal staff to support the transition, and gaining early buy-in from business units.
4. How does Open Innovation complement and extend the capabilities of open science, and why is this relationship important for translating scientific knowledge into market success?
While open science generates and rapidly disseminates knowledge, Open Innovation plays a crucial role in commercializing that knowledge by creating a business model for its use and capture. The transition from lab to market faces a “Valley of Death” due to funding gaps, IP assignment challenges, and the shift from scientific to commercial incentives. By defining a value proposition and revenue model, Open Innovation provides the mechanism and motivation for startups and investors to commercialize scientific discoveries. Moreover, Open Innovation complements open science by offering structured pathways to identify promising applications, manage IP and risk, attract resources, and navigate regulatory structures, thereby facilitating the successful translation of scientific knowledge into real-world impact.
5. How does the unique role of the CCP in China’s innovation landscape create both opportunities and challenges for Open Innovation?
In the unique Chinese context, the Chinese Communist Party (CCP) plays a significant role in influencing Open Innovation, unlike in Western economies. The CCP’s centralized approach can foster rapid progress in industries like high-speed rail, where governmental support and coordination are crucial. However, this top-down approach can stifle innovation in sectors like automotive and semiconductors, where market forces and private companies are the primary drivers of innovation. The CCP’s focus on control can hinder the dissemination and absorption of new technologies, particularly when it favors state-owned enterprises over more dynamic, innovative privately-owned firms. For China to sustain its growth, the CCP needs to embrace more decentralized, market-driven Open Innovation approaches, balancing its leading role with the greater use of market signals, especially supporting the POCs in each industry.
1. What is the ‘Exponential Paradox’, and what are its underlying causes?
The ‘Exponential Paradox’ highlights the disconnect between the rapid pace of technological development and the slow growth in productivity and incomes. This is because while there’s been heavy investment in generating new innovations, insufficient attention has been paid to disseminating them throughout society and enabling its absorption into business and operations. This shortfall in dissemination and absorption is due to insufficient investment in the knowledge infrastructure of the society, especially in the capabilities of its citizens, its universities, its research labs and its supporting institutions. To overcome this paradox, we need to invest more effectively in disseminating the knowledge widely, and enhancing the absorption of that knowledge in many more organizations and for many more citizens. Without addressing the absorption bottleneck, our societies will not achieve the full potential of the new technologies being created.
2. How does Open Innovation differ from traditional, closed innovation models, and what are its key characteristics?
Open Innovation is a distributed innovation process where firms use both external and internal knowledge, ideas, and paths to market. It differs from closed innovation, which primarily relies on internal R&D. Key aspects of Open Innovation include: 1) recognizing the abundance of external knowledge, 2) utilizing business models to translate technology into value, 3) managing inflows and outflows of knowledge, and 4) strategically leveraging IP. It involves both “outside-in” and “inside-out” processes, where external knowledge is brought into the firm, and internal knowledge is made available to others, respectively. Successful Open Innovation requires overcoming challenges such as Not-Invented-Here syndrome, organizational silos, and funding bottlenecks.
3. What are the critical challenges associated with the ‘back end’ of Open Innovation, and how can these be addressed?
The “back end” of Open Innovation focuses on transferring innovations from R&D or innovation units to the business units responsible for commercialization. This stage is crucial for realizing business results but often encounters significant hurdles. Common challenges include: 1) organizational resistance from employees threatened by external knowledge or fearing job displacement; 2) funding gaps, as innovative projects often struggle to secure ongoing funding from business units; and 3) lack of senior management support to champion innovative projects and navigate internal politics. Successfully navigating the back end requires strategies like creating dedicated funding for project transfers, assigning internal staff to support the transition, and gaining early buy-in from business units.
4. How does Open Innovation complement and extend the capabilities of open science, and why is this relationship important for translating scientific knowledge into market success?
While open science generates and rapidly disseminates knowledge, Open Innovation plays a crucial role in commercializing that knowledge by creating a business model for its use and capture. The transition from lab to market faces a “Valley of Death” due to funding gaps, IP assignment challenges, and the shift from scientific to commercial incentives. By defining a value proposition and revenue model, Open Innovation provides the mechanism and motivation for startups and investors to commercialize scientific discoveries. Moreover, Open Innovation complements open science by offering structured pathways to identify promising applications, manage IP and risk, attract resources, and navigate regulatory structures, thereby facilitating the successful translation of scientific knowledge into real-world impact.
5. How does the unique role of the CCP in China’s innovation landscape create both opportunities and challenges for Open Innovation?
In the unique Chinese context, the Chinese Communist Party (CCP) plays a significant role in influencing Open Innovation, unlike in Western economies. The CCP’s centralized approach can foster rapid progress in industries like high-speed rail, where governmental support and coordination are crucial. However, this top-down approach can stifle innovation in sectors like automotive and semiconductors, where market forces and private companies are the primary drivers of innovation. The CCP’s focus on control can hinder the dissemination and absorption of new technologies, particularly when it favors state-owned enterprises over more dynamic, innovative privately-owned firms. For China to sustain its growth, the CCP needs to embrace more decentralized, market-driven Open Innovation approaches, balancing its leading role with the greater use of market signals, especially supporting the POCs in each industry.
Key Takeaways
1. Technological advancements don’t automatically lead to economic prosperity.
The paradox highlights the importance of going beyond simply generating new technologies. For an innovation to deliver economic and social value, it must be widely disseminated and effectively absorbed by organizations and individuals. An overemphasis on generation, without sufficient investment in dissemination and absorption leads to stalled productivity growth, as seen in many Western economies. Recognizing this paradox requires organizations to invest in knowledge infrastructure, training programs, and change management initiatives to facilitate the successful adoption and implementation of new technologies.
Practical Application:
An AI development team can use the concept of the Exponential Paradox to evaluate their innovation pipeline. Are they investing too heavily in developing new algorithms without considering how those algorithms will be disseminated throughout the company or absorbed into different product lines? If so, the team might consider refocusing resources towards activities that enable dissemination and absorption, such as creating user-friendly tools, documentation, training programs, and support structures for other teams to effectively utilize the new algorithms.
2. Open Innovation is crucial for maximizing the value of innovation.
Open Innovation recognizes the value of external knowledge and collaborations. By leveraging external sources like startups, universities, and even competitors, organizations can access new ideas, technologies, and markets, saving time, reducing costs, and sharing risks. This requires a shift in mindset from closed, internal innovation towards embracing external partnerships and knowledge flows. Successful Open Innovation also involves strategically managing inflows and outflows of knowledge and intellectual property to align with the organization’s business model.
Practical Application:
An AI product engineer can leverage external knowledge sources, such as open-source libraries, research papers, and online communities, to enhance their own product development. They can identify and adapt existing algorithms and tools rather than building everything from scratch, thereby reducing development time and cost. They also can contribute back to the open-source community by sharing their own code and tools, thereby further enhancing the community’s collective knowledge.
3. Lean Startup minimizes waste by focusing on customer feedback and iterative development.
Lean Startup, emphasizing iterative development and customer feedback, reduces waste by focusing on delivering a minimum viable product (MVP) and validating it with customers early on. This approach avoids investing heavily in products or features that customers don’t want. Applying Lean Startup principles within established companies requires adaptation to address challenges such as existing business models, internal processes, and organizational culture. Large companies can utilize Lean Startup within their innovation processes by testing new products/services with early adopters, while utilizing more established, efficient processes for sustaining products and services already released and scaled in the market.
Practical Application:
An AI startup can use Lean Startup principles to develop a machine learning model by focusing on delivering a minimum viable product (MVP) to early adopters. Instead of aiming for a perfect model from the start, they can collect user feedback, iterate on the MVP based on real-world usage data and then scale the model once its value proposition and revenue models have been validated in the market.
4. The ‘back end’ of innovation is crucial for realizing tangible results.
Successfully implementing Open Innovation, or any innovation, requires careful attention to the often-neglected “back end” of the process. This involves transferring innovations to the business units responsible for commercialization, integrating them into existing systems, and scaling them to achieve widespread impact. Common challenges include organizational resistance, funding gaps, and insufficient support from senior management. Addressing these challenges requires new processes and mechanisms to facilitate knowledge transfer, secure funding for scaling projects, and gain leadership buy-in.
Practical Application:
In AI product development, it’s not enough to develop cutting-edge algorithms. The product manager also needs to consider dissemination and absorption strategies. Are there user-friendly interfaces, documentation, and training materials to help users understand and utilize the AI capabilities? Are there feedback mechanisms in place to gather user insights and iterate on the product accordingly? Successfully integrating AI into a product requires careful planning and execution of these ‘back end’ processes.
5. Strategic engagement with startups can boost corporate innovation.
Collaborations with startups offer large companies opportunities to access new technologies, talent, and market insights. However, successful partnerships require careful consideration of the engagement model and the needs of both parties. Equity-based models like Corporate Venture Capital provide more control but may limit a startup’s agility and openness to collaborate with other companies. Lightweight models such as startup programs and accelerators can be more effective by enabling faster collaborations, broader engagement, and a focus on creating proofs-of-concept rather than seeking immediate financial returns.
Practical Application:
An AI company seeking to expand into new markets can leverage partnerships with startups. For example, the corporation might partner with a startup specializing in data annotation or model deployment to gain access to their expertise and technologies. By collaborating, the established company can avoid building these capabilities in-house, saving time and resources. The startup, in turn, gains access to the corporation’s resources, market reach, and customer base.
1. Technological advancements don’t automatically lead to economic prosperity.
The paradox highlights the importance of going beyond simply generating new technologies. For an innovation to deliver economic and social value, it must be widely disseminated and effectively absorbed by organizations and individuals. An overemphasis on generation, without sufficient investment in dissemination and absorption leads to stalled productivity growth, as seen in many Western economies. Recognizing this paradox requires organizations to invest in knowledge infrastructure, training programs, and change management initiatives to facilitate the successful adoption and implementation of new technologies.
Practical Application:
An AI development team can use the concept of the Exponential Paradox to evaluate their innovation pipeline. Are they investing too heavily in developing new algorithms without considering how those algorithms will be disseminated throughout the company or absorbed into different product lines? If so, the team might consider refocusing resources towards activities that enable dissemination and absorption, such as creating user-friendly tools, documentation, training programs, and support structures for other teams to effectively utilize the new algorithms.
2. Open Innovation is crucial for maximizing the value of innovation.
Open Innovation recognizes the value of external knowledge and collaborations. By leveraging external sources like startups, universities, and even competitors, organizations can access new ideas, technologies, and markets, saving time, reducing costs, and sharing risks. This requires a shift in mindset from closed, internal innovation towards embracing external partnerships and knowledge flows. Successful Open Innovation also involves strategically managing inflows and outflows of knowledge and intellectual property to align with the organization’s business model.
Practical Application:
An AI product engineer can leverage external knowledge sources, such as open-source libraries, research papers, and online communities, to enhance their own product development. They can identify and adapt existing algorithms and tools rather than building everything from scratch, thereby reducing development time and cost. They also can contribute back to the open-source community by sharing their own code and tools, thereby further enhancing the community’s collective knowledge.
3. Lean Startup minimizes waste by focusing on customer feedback and iterative development.
Lean Startup, emphasizing iterative development and customer feedback, reduces waste by focusing on delivering a minimum viable product (MVP) and validating it with customers early on. This approach avoids investing heavily in products or features that customers don’t want. Applying Lean Startup principles within established companies requires adaptation to address challenges such as existing business models, internal processes, and organizational culture. Large companies can utilize Lean Startup within their innovation processes by testing new products/services with early adopters, while utilizing more established, efficient processes for sustaining products and services already released and scaled in the market.
Practical Application:
An AI startup can use Lean Startup principles to develop a machine learning model by focusing on delivering a minimum viable product (MVP) to early adopters. Instead of aiming for a perfect model from the start, they can collect user feedback, iterate on the MVP based on real-world usage data and then scale the model once its value proposition and revenue models have been validated in the market.
4. The ‘back end’ of innovation is crucial for realizing tangible results.
Successfully implementing Open Innovation, or any innovation, requires careful attention to the often-neglected “back end” of the process. This involves transferring innovations to the business units responsible for commercialization, integrating them into existing systems, and scaling them to achieve widespread impact. Common challenges include organizational resistance, funding gaps, and insufficient support from senior management. Addressing these challenges requires new processes and mechanisms to facilitate knowledge transfer, secure funding for scaling projects, and gain leadership buy-in.
Practical Application:
In AI product development, it’s not enough to develop cutting-edge algorithms. The product manager also needs to consider dissemination and absorption strategies. Are there user-friendly interfaces, documentation, and training materials to help users understand and utilize the AI capabilities? Are there feedback mechanisms in place to gather user insights and iterate on the product accordingly? Successfully integrating AI into a product requires careful planning and execution of these ‘back end’ processes.
5. Strategic engagement with startups can boost corporate innovation.
Collaborations with startups offer large companies opportunities to access new technologies, talent, and market insights. However, successful partnerships require careful consideration of the engagement model and the needs of both parties. Equity-based models like Corporate Venture Capital provide more control but may limit a startup’s agility and openness to collaborate with other companies. Lightweight models such as startup programs and accelerators can be more effective by enabling faster collaborations, broader engagement, and a focus on creating proofs-of-concept rather than seeking immediate financial returns.
Practical Application:
An AI company seeking to expand into new markets can leverage partnerships with startups. For example, the corporation might partner with a startup specializing in data annotation or model deployment to gain access to their expertise and technologies. By collaborating, the established company can avoid building these capabilities in-house, saving time and resources. The startup, in turn, gains access to the corporation’s resources, market reach, and customer base.
Suggested Deep Dive
Chapter: The Back End of Open Innovation
Given the focus of this summary request is an AI product manager, it would be very beneficial to delve into this section and the problems and opportunities it presents in a high-growth, technology focused industry.
Memorable Quotes
Introduction. 13
What is needed is a renewal of our understanding of Open Innovation, and how we can get better business results from using Open Innovation.
Open Innovation in the Twenty-First Century. 39
The platform models are more open, because they entice numerous third parties to innovate on your architecture, your system, your platform. And they often enable others to license unused technologies from you to place those into other business models. This makes continued investment in R&D more sustainable, and can even confer competitive advantage.
Open Innovation in the Twenty-First Century. 45
In the Open Innovation model, projects can be launched from either internal or external technology sources, and new technology can enter into the process at various stages.
Lean Startup and Open Innovation. 50
Just as the product must be developed, so too must a startup company identify and seek out customers willing and able to buy its offerings.
From Open Science to Open Innovation. 86
Knowledge must not only be generated and disseminated, it must also be absorbed and put to work.
Introduction. 13
What is needed is a renewal of our understanding of Open Innovation, and how we can get better business results from using Open Innovation.
Open Innovation in the Twenty-First Century. 39
The platform models are more open, because they entice numerous third parties to innovate on your architecture, your system, your platform. And they often enable others to license unused technologies from you to place those into other business models. This makes continued investment in R&D more sustainable, and can even confer competitive advantage.
Open Innovation in the Twenty-First Century. 45
In the Open Innovation model, projects can be launched from either internal or external technology sources, and new technology can enter into the process at various stages.
Lean Startup and Open Innovation. 50
Just as the product must be developed, so too must a startup company identify and seek out customers willing and able to buy its offerings.
From Open Science to Open Innovation. 86
Knowledge must not only be generated and disseminated, it must also be absorbed and put to work.
Comparative Analysis
Compared to earlier works on innovation like Clayton Christensen’s The Innovator’s Dilemma, which focuses on disruptive innovation and its impact on established firms, “Open Innovation Results” expands the scope to include the broader innovation ecosystem and the importance of collaboration. It also contrasts with internal, resource-based views of innovation, arguing that external knowledge flows are crucial for success. While von Hippel’s “Democratizing Innovation” explores user-driven innovation, this book complements it by adding the inside-out dimension and the role of business models. Finally, unlike many academic studies that focus primarily on successful cases, “Open Innovation Results” examines failures and limitations of Open Innovation, providing a more balanced and realistic perspective.
Reflection
While “Open Innovation Results” offers valuable insights into innovation management, readers should approach certain claims with caution. The book’s optimistic view of Open Innovation’s potential might overlook the significant challenges in implementation, particularly within large, established organizations. The Exponential Paradox, while highlighting a real issue, might oversimplify the complex relationship between technological advancement and economic growth, neglecting factors beyond innovation management. The book’s emphasis on the “back end” of innovation is crucial but could be strengthened by offering more concrete solutions for addressing organizational barriers and managing knowledge transfer. Despite these limitations, “Open Innovation Results” makes a significant contribution by providing practical insights, grounded in real-world examples, and challenging traditional views of innovation. Its emphasis on dissemination, absorption, and customer development resonates particularly strongly in today’s rapidly changing technological landscape, where the successful commercialization of AI and other advanced technologies requires more than just scientific breakthroughs but effective strategies for adoption, implementation, and scale-up.
Flashcards
What is the Exponential Paradox?
Stagnating economic productivity and income growth despite rapid technological advancements.
What is Open Innovation?
A distributed innovation process based on managed knowledge flows across organizational boundaries.
What is outside-in Open Innovation?
Involves bringing external knowledge into the firm’s innovation process.
What is inside-out Open Innovation?
Making internal knowledge available to external parties.
What is the ‘back end’ of Open Innovation?
The challenges in transferring innovations from R&D to business units for commercialization.
What are three common challenges in the ‘back end’ of Open Innovation?
Organizational resistance, funding gaps, and lack of senior management support.
What is a Minimal Viable Product (MVP) in Lean Startup?
A minimum set of features that will compel a customer to buy a product.
What are two traditional models of corporate engagement with startups?
Corporate Venture Capital (CVC) and inside-out corporate incubators.
What are two lightweight models for engaging with startups?
Startup programs (outside-in and platform-based) and corporate accelerator programs.
What characterizes Open Innovation with Chinese characteristics?
Balances market forces with CCP guidance and often involves state-owned enterprises.
What is the Exponential Paradox?
Stagnating economic productivity and income growth despite rapid technological advancements.
What is Open Innovation?
A distributed innovation process based on managed knowledge flows across organizational boundaries.
What is outside-in Open Innovation?
Involves bringing external knowledge into the firm’s innovation process.
What is inside-out Open Innovation?
Making internal knowledge available to external parties.
What is the ‘back end’ of Open Innovation?
The challenges in transferring innovations from R&D to business units for commercialization.
What are three common challenges in the ‘back end’ of Open Innovation?
Organizational resistance, funding gaps, and lack of senior management support.
What is a Minimal Viable Product (MVP) in Lean Startup?
A minimum set of features that will compel a customer to buy a product.
What are two traditional models of corporate engagement with startups?
Corporate Venture Capital (CVC) and inside-out corporate incubators.
What are two lightweight models for engaging with startups?
Startup programs (outside-in and platform-based) and corporate accelerator programs.
What characterizes Open Innovation with Chinese characteristics?
Balances market forces with CCP guidance and often involves state-owned enterprises.