Blockchain concept with a skyline background

Driving Innovation: Exploring essential theories in innovation management for blockchain and automation

Getting your Trinity Audio player ready...

This article was first published on Dr. Craig Wright’s blog, and we republished with permission from the author.


This paper explores the fundamental theories and concepts underpinning innovation management and their application to emerging technologies such as blockchain and automation technologies. It examines the theories of the Innovation Ecosystem, Organizational Culture, Open Innovation, Diffusion of Innovations, Disruptive Innovation, and Resource-Based View, highlighting their relevance in understanding the challenges and opportunities presented by such technologies. The paper emphasizes the importance of fostering strong ecosystem connections, cultivating an innovative culture, embracing open innovation approaches, understanding technology diffusion dynamics, leveraging disruptive potential, and harnessing valuable resources. By integrating these theories into innovation management strategies, businesses can navigate the complexities of implementing blockchain and automation technologies, enhance efficiency and competitiveness, and drive sustainable growth. In addition, ongoing research and adaptability are essential to keep pace with technological advancements in this rapidly evolving field.

Keywords: Innovation Management, Blockchain, Automation, Innovation Ecosystem, Organisational Culture, Open Innovation, Technology Diffusion, Disruptive Innovation, Resource-Based View.

Innovation Management and Strategy1


Innovation management is a dynamic field that fosters and guides innovation within organizations. To navigate the ever-changing landscape of technological advancements, businesses must understand and apply fundamental theories and concepts that underpin the field (Curley & Salmelin, 2017). This paper explores the essential theories in innovation management and their relevance to emerging technologies, specifically blockchain, and automation.

The paper begins by discussing the significance of an innovation ecosystem in driving successful innovation. Innovation Ecosystem Theory emphasizes the interconnectedness of businesses, institutions, and stakeholders, highlighting the importance of strategic partnerships and collaborations (Fernandes & Ferreira, 2022). Understanding the ecosystem dynamics becomes crucial in leveraging the potential of blockchain and automation technologies.

Organizational culture plays a pivotal role in facilitating innovation. The Organizational Culture Theory examines psychological safety, collectivism, and power distance and their impact on fostering an innovative culture (Çakar & Ertürk, 2010). Building a supportive and inclusive environment encourages experimentation and accelerates innovation in the context of blockchain and automation.

Open Innovation Theory challenges the traditional notion that innovation is solely driven by internal research and development. Instead, this theory advocates for incorporating external ideas and collaboration with experts, including academia, start-ups, and competitors (De Jong et al., 2008). Such open innovation approaches can contribute to developing and advancing blockchain and automation technologies.

Understanding the Diffusion of Innovations Theory is vital to effectively market and adopt new technologies. As blockchain and automation are still emerging, their widespread adoption depends on technical compatibility, perceived benefits, and cultural acceptance. Companies that comprehend these dynamics can strategically drive adoption and market these technologies (Wang et al., 2019). Alternatively, Disruptive Innovation Theory highlights the potential of blockchain and automation to disrupt industries by enabling new business models (Schmidt & Van Der Sijde, 2022). By targeting neglected market segments, smaller companies can challenge established incumbents. This theory shows how blockchain and automation can reshape various sectors, driving transformative changes (Sáez & Inmaculada, 2020). Lastly, the Resource-Based View theory emphasizes leveraging unique resources and capabilities to gain a competitive advantage. By deploying the technology associated with blockchain and automation, organizations can harness their technical expertise, intellectual property, and access to large datasets to develop proprietary algorithms or technologies (Ho et al., 2022).

This paper delves into these theories and their implications for innovation management in the context of blockchain and automation. First, it explores how companies can apply these theories to enhance efficiency, competitiveness, and sustainable growth. The subsequent sections will detail each approach, examining its foundations, practical applications, and potential impact on innovation management strategies. By integrating these theories, organizations can navigate the complexities of implementing new technologies and position themselves at the forefront of innovation (Rehman Khan et al., 2022). The paper concludes by arguing that understanding these theories and their application to blockchain and automation is essential for organizations seeking to thrive in an increasingly innovative and technologically driven business environment.

Part 1 – The elements of innovation management strategy

Innovation management strategy plays a vital role in organizations by providing a systematic and purposeful approach to foster and guide innovation within their operations. It encompasses various elements essential for cultivating a culture of innovation and driving organizational growth. This paper explores the critical components of an innovation management strategy and their significance in promoting and supporting innovation (Dombrowski et al., 2007).

First and foremost, an effective innovation management strategy begins with a clear vision and well-defined objectives. This involves articulating the organization’s innovation goals, aspirations, and desired outcomes. By identifying the type of innovations sought, such as product, process, or business model innovations, and specifying the strategic focus areas, the organization can align its efforts toward achieving meaningful innovation. Building an innovation-friendly culture and demonstrating strong leadership is crucial to an innovation management strategy (George et al., 2012). Creating an environment that encourages and rewards creativity, risk-taking, and experimentation is essential for inspiring employees to think outside the box. In addition, leadership is vital in setting the tone, supporting the innovation agenda, allocating necessary resources, and fostering a collaborative and open work atmosphere (Martins & Terblanche, 2003).

Resource allocation is a critical component of innovation management strategy. Allocating dedicated resources, including budget, time, and talent, ensures that innovation initiatives receive the necessary support and attention. Moreover, when combined with the resources to explore new ideas, providing time for employees allows organizations to unleash their innovative potential and drive progress (Nagji & Tuff, 2012).

Idea generation and management are integral to an innovation management strategy. Establishing mechanisms to capture, evaluate, and prioritize ideas from both internal and external sources is essential. This may involve conducting idea-generation workshops, implementing suggestion programs, leveraging crowdsourcing platforms, or utilizing innovation management platforms (Zahra & Nambisan, 2012). These tools help manage the idea pipeline, facilitate collaboration, and ensure that innovative ideas are effectively harnessed and transformed into tangible outcomes.

Collaboration and knowledge sharing is vital for fostering innovation. Encouraging cross-functional collaboration and facilitating the exchange of ideas, expertise, and best practices can significantly enhance innovation efforts. Regular communication channels, dedicated innovation teams, and collaboration platforms allow employees to share insights, collaborate on projects, and leverage collective intelligence. Experimentation and prototyping form another crucial element of an innovation management strategy (Davila et al., 2012). Organizations can test and refine new ideas by creating a safe space for experimentation before full-scale implementation. This iterative process allows for learning from failures, minimizing risks, and enables the development of innovative solutions that can drive growth and competitive advantage.

In conclusion, an effective innovation management strategy encompasses various elements to stimulate and support organizational innovation (De Jong et al., 2008). By defining vision and objectives, building an innovation-friendly culture, allocating dedicated resources, implementing idea generation and management mechanisms, promoting collaboration and knowledge sharing, and encouraging experimentation and prototyping, organizations can unlock their innovative potential and pave the way for sustained success in a rapidly evolving business landscape (Nagji & Tuff, 2012).

Part 2 – The principles of continuous improvement

Continuous improvement is guided by fundamental principles that form the basis of its approach. These principles are essential for organizations seeking to cultivate a culture of enduring growth and development. This essay will explore the fundamental principles of continuous improvement and their significance in driving organizational excellence (Teece, 2010, 2019). One of the basic principles of continuous improvement is Kaizen (Berger, 1997). Derived from the Japanese language, Kaizen translates to “change for the better” or “continuous improvement” (Prayuda, 2020). It emphasizes the philosophy of making regular, incremental improvements. This approach encourages all employees to contribute to improvement efforts, fostering a culture of continuous learning and innovation throughout the organization.

Problem-solving is another critical principle in continuous improvement. It involves proactively identifying and addressing problems and challenges. This principle emphasizes using structured problem-solving techniques, including root cause analysis, in understanding the underlying causes of issues and developing practical solutions (de Mast & Lokkerbol, 2012). Organizations can effectively address recurring problems and prevent their reoccurrence by adopting a systematic problem-solving approach.

Data-driven decision-making is a vital aspect of continuous improvement. It relies on data and evidence to drive decision-making processes. Organizations collect and analyze relevant data to identify trends, patterns, and areas for improvement (VanStelle et al., 2012). This data-driven approach helps make informed decisions, monitor the impact of improvement initiatives, and identify other areas of enhancement. Feedback and collaboration are integral components of continuous improvement. Open communication and collaboration are encouraged across all levels of the organization. Seeking feedback from employees, customers, and stakeholders provides valuable insights and ideas for improvement. Collaboration helps leverage diverse perspectives and experiences to generate innovative solutions and drive improvement efforts effectively (Cross et al., 2010).

Standardization and documentation play a significant role in continuous improvement. Standardization involves establishing consistent processes and procedures within the organization. Organizations can reduce variability and ensure consistent quality and performance by standardizing operations. Documentation of best practices is equally important, as it enables sharing knowledge and replicating successful improvements throughout the organization (Gephart et al., 1996). Continuous improvement also emphasizes learning and development. It fosters a culture of constant learning, where individuals and teams are encouraged to develop new skills, acquire knowledge, and stay updated with industry trends. Learning and development initiatives enable employees to contribute effectively to improvement efforts and drive organizational innovation.

In summary, continuous improvement is guided by several fundamental principles crucial for organizations seeking to drive ongoing growth and excellence. These principles include Kaizen, problem-solving, data-driven decision-making, feedback and collaboration, standardization and documentation, and learning and development (Gephart et al., 1996). By embracing these principles, organizations can create a culture of continuous improvement, leading to enhanced performance, innovation, and long-term success. Furthermore, continuous improvement is not a one-time project but an ongoing, cyclical process. It involves regularly reviewing performance, setting improvement goals, implementing changes, measuring outcomes, and initiating further improvements. This iterative process helps organizations adapt to changing market conditions, enhance efficiency, quality, and customer satisfaction, and stay competitive in a dynamic business environment (Bhuiyan & Baghel, 2005).

Part 3 – Key areas in innovation management

Innovation management encompasses several key areas crucial for organizations striving to foster and drive innovation. This essay will delve into these areas and highlight the gaps in current knowledge that present opportunities for further exploration and understanding (Mohr & Sarin, 2009). One significant area of innovation management is innovation ecosystems. These ecosystems comprise networks of organizations, including firms, universities, and government agencies, collaborating on innovation activities. While research on innovation ecosystems has grown in recent years, much remains to learn about how these ecosystems function and how different organizations within them interact. As a result, managing innovation ecosystems effectively remains a topic of exploration, along with understanding the dynamics and impact of such collaboration.

Open innovation is another vital area of focus. It advocates for the inflow and outflow of knowledge to accelerate internal innovation and develop markets for the external use of innovation. Although there has been considerable research on open innovation in large firms, less is known about in what manner small and medium-sized enterprises (SMEs) can engage in open innovation. Furthermore, exploring how open innovation can be applied in non-profit or governmental contexts presents an avenue for future investigation (Chesbrough, 2003).

Organizational culture and leadership play a crucial role in promoting or stifling innovation. While this is a well-established topic, there is always room for a more nuanced understanding. For instance, the influence of leadership behavior on employees’ innovative behavior in remote work contexts warrants exploration. Additionally, understanding how organizations can maintain a creative culture during times of crisis or rapid change is an area that requires further investigation (Mumford et al., 2002). Finally, digital innovation has significantly transformed the innovation landscape. Understanding the unique aspects of digital invention compared to traditional creation, its impact on business models, and effective management strategies are all areas ripe for exploration and study. Further research can provide valuable insights for organizations navigating the digital era (Yukl, 2008).

The intersection of sustainability and innovation is an emerging area of interest. With the growing awareness of environmental issues, understanding how innovation can contribute to sustainability is vital. Research on eco-innovation, sustainable business models, and the role of regulation in promoting or hindering sustainability-oriented innovation is essential for addressing pressing global challenges. Measuring innovation is an ongoing challenge to innovation management (Tamayo-Orbegozo et al., 2017). Developing methods and metrics to assess innovation performance and determine critical indicators of successful innovation remains a topic of interest. Continual exploration and refinement can provide organizations with valuable tools for evaluating innovation efforts.

Lastly, innovation management encompasses diverse areas that warrant further exploration and understanding (Del Vecchio et al., 2018). By addressing gaps in knowledge within innovation ecosystems, open innovation, innovation culture and leadership, digital innovation, sustainability and innovation, and innovation measurement and metrics, organizations can successfully enhance their innovation capabilities and navigate the evolving innovation landscape (Papadonikolaki et al., 2022).

Part 4 – Exploring Opportunities in Blockchain Systems and Automation

Applying blockchain systems and automation to enhance business efficiency and reduce losses is an expanding field that offers numerous research opportunities. By effectively managing the strategies associated with these technologies, companies can integrate them into their operations to streamline processes and minimize waste. This section highlights critical areas where further investigation is needed to fully understand their implementation and potential benefits (Papadonikolaki et al., 2022).

Supply chain management stands out as one of the most promising applications of blockchain technology. By leveraging blockchain, companies can achieve transparency, traceability, and operational efficiency in their supply chains. However, there is a need for further research to identify best practices for implementing blockchain across various types of supply chains. Additionally, understanding the impact of blockchain on supply chain performance and finding ways to overcome barriers to its adoption are essential considerations in this area (Rehman Khan et al., 2022).

Smart contracts offer great potential for automating business processes and reducing losses arising from fraud or errors. These self-executing electronic data interchange (EDI) systems incorporate contractual terms directly into the code (Law, 2017). However, there are lingering questions regarding their legal status, security, and the specific business processes for which they are most suitable. Further research can shed light on these aspects, ensuring the effective utilization of smart contracts in various contexts (Sklaroff, 2017).

The concept of decentralized and secure data sharing facilitated by blockchain technology can potentially revolutionize multiple industries. However, businesses must navigate the trade-offs between data sharing and privacy. Therefore, research is needed to effectively develop frameworks and strategies for managing these considerations. Furthermore, ensuring compliance with data protection regulations becomes crucial in leveraging blockchain for data-sharing purposes (A. Kumar et al., 2020).

As more businesses adopt blockchain systems, the need for interoperability between these systems becomes increasingly evident. Research opportunities lie in exploring standards, protocols, and mechanisms for achieving blockchain interoperability (A. Kumar et al., 2020; N. Kumar, 2020). Additionally, investigating the business implications of interoperability can help organizations assess the benefits and challenges associated with integrating blockchain systems across different platforms and networks.

Automation technologies, including blockchain, can disrupt traditional job markets and displace many conventional roles. As a result, businesses need to manage this transition and equip their employees with the necessary skills for the future (Børing, 2017). Research can focus on understanding how companies can effectively navigate this transformation, ensuring a smooth transition and providing guidance on the skills employees require in the evolving job landscape.

The energy consumption of blockchain technologies, particularly those employing proof-of-work consensus mechanisms like Bitcoin, has raised concerns about sustainability. Therefore, further investigation into the energy implications of blockchain systems is necessary to assess their environmental impact and explore ways to enhance energy efficiency. Organizations can adopt blockchain technologies by addressing these concerns while minimizing their ecological footprint (Sarkodie & Owusu, 2022).

Applying blockchain systems and automation presents exciting opportunities for improving business processes and reducing losses (Ho et al., 2022). Through research efforts focusing on supply chain management, smart contracts, data sharing and privacy, interoperability, job displacement, and energy use and sustainability, organizations can gain deeper insights into the effective implementation of these technologies and their long-term impact on various business operations (A. Kumar et al., 2020; V. Kumar & Raheja, 2012).

Part 5 – The purpose behind analyzing the literature

Innovation management research literature plays a vital role in guiding the implementation of blockchain systems and automation to improve business efficiency and reduce loss (Attaran, 2020). By examining the existing research, organizations can gain valuable insights into applying these technologies in various areas. For example, innovation management research guides strategic planning when examining business practices. Understanding the potential disruptions and competitive advantages blockchain and automation can bring to different industries is crucial for effective planning.

Additionally, research helps businesses navigate the challenges of implementing and adopting these technologies, including selecting the right technology, managing the change process, and aligning the technology with the overall business strategy and culture (Cabrera et al., 2001). Finally, risk management is another area where research plays a key role. Businesses can develop effective mitigation strategies to address technological, legal, regulatory, and business risks by identifying common risks associated with blockchain and automation (Mendling et al., 2018).

Innovation management research highlights the potential for inclusive innovation in social change initiatives. Blockchain enables secure and decentralized data sharing, empowering individuals and communities. When implemented thoughtfully, automation can free up human time for more valuable activities. Research guides these initiatives by exploring methods to involve diverse stakeholders in the innovation process and understanding the societal implications of these technologies (Mohr & Sarin, 2009). Policymakers and regulators also rely on research to make informed decisions regarding policy and regulation related to blockchain and automation. Research helps them understand the broader implications of these technologies, such as their impact on jobs, income distribution, and energy consumption.

It is important to note that the applicability of research findings will depend on the specific context of each organization or social change initiative. Academic research should be complemented with insights from practitioners, industry reports, case studies, and other sources of knowledge. Continuous learning is essential as blockchain and automation technologies evolve rapidly, ensuring organizations stay up-to-date with the latest developments and understand their potential implications (Mohr & Sarin, 2009).

In summary, innovation management research provides valuable insights for organizations and social change initiatives looking to leverage blockchain systems and automation (Anceaume et al., 2017). By considering the research findings, businesses can make informed decisions regarding strategic planning, implementation, adoption, risk management, and social impact considerations. However, it is crucial to consider the specific context and supplement academic research with other sources of knowledge to maximize the benefits of these transformative technologies.

Part 6 – The impact of change

Ongoing research focuses on the potential impact of innovation in various areas. The first study examined the influence of a firm’s connections to its ecosystem on its innovation capabilities. It found that plant-based protein firms had a stronger innovation orientation than traditional food producers, suggesting that industry associations, government, and other agricultural companies play a significant role in fostering innovation. This study highlights the importance of cultivating strong ties with ecosystem actors to enhance innovation potential and may lead to innovation management strategies focused on networking and collaboration (Youtie et al., 2023).

The second study investigated the role of organizational culture factors in shaping the social and performance management context, ultimately affecting innovation performance. It emphasized creating a supportive and inclusive culture to drive innovation. The findings suggest that organizations may need to rethink their culture and management practices to foster innovation, potentially leading to the adoption of more people-centric innovation management strategies (Zhang et al., 2023).

A systematic literature review constituted the third study, exploring the relationship between management innovation, firm performance, and other forms of innovation. The examination revealed that management innovation is a growing field. Furthermore, it identified several areas for future research, including the conceptualization, definitions, and measurements of management innovation and its drivers, antecedents, and role as a mediator/moderator variable. This review could contribute to a more nuanced understanding of how management innovation impacts firm performance and interacts with other types of innovation. Consequently, it may lead to the developing of more effective and nuanced innovation management strategies (Henao-García & Cardona Montoya, 2023).

These research findings hold the potential to significantly impact innovation management practices. They may inspire a shift towards holistic approaches considering various factors, such as ecosystem connections, organizational culture, and management practices. Additionally, they may stimulate further research in underexplored areas, driving advancements in the field. By implementing these insights, organizations can enhance innovation management practices, improving business efficiency and competitiveness (Tiwari, 2022).

Part 7 – Approaching innovation and technology

O’Sullivan and Dooley (2008) focus on the practical aspects of implementing innovation within organizations. The authors emphasize the need for a structured approach to integrating innovation into a company’s core operations and culture. The authors explore various strategies and tools to foster innovation, including idea generation, design thinking, prototyping, and collaboration. They highlight the importance of creating an environment encouraging experimentation, risk-taking, and learning from failures. O’Sullivan and Dooley emphasize that innovation should not be limited to specific departments or individuals but should involve all employees across the organization. They stress the significance of leadership support and the establishment of clear goals and metrics to measure the impact of innovation initiatives.

Forcadell and Guadamillas (2002) provide a case study on implementing a knowledge management strategy oriented to innovation. This explores how organizations can leverage knowledge management practices to drive innovation by examining the challenges companies face when promoting innovation and highlighting the crucial role that knowledge management plays in this process. They emphasize that effective knowledge management can facilitate creating, sharing, and applying knowledge within an organization, leading to increased innovation capabilities. The case study presents a real-world example of an organization implementing a knowledge management strategy that fosters innovation. It discusses the steps taken, such as identifying and capturing relevant knowledge, organizing and categorizing it, and making it accessible to employees across the company.

This work stresses the importance of creating a culture that values knowledge sharing and collaboration, as well as the need for leadership support to drive the implementation of the strategy. They also highlight the role of technology in supporting knowledge management efforts, including using tools for knowledge sharing, collaboration, and learning. Kaplan (1998)  explores the concept of innovation action research and its potential to generate new theories and practices in management. By emphasizing the importance of combining practical action with rigorous research to drive innovation in management, Kaplan argues that traditional research methods alone may not be sufficient to address complex management challenges and that action research, which involves actively implementing and testing new ideas in real-world settings, can provide valuable insights and lead to the development of new theories and practices. The research notes that the role of skillful managers is critical in driving innovation by actively engaging in experimentation, learning, and adaptation. Kaplan suggests that managers who are open to new ideas and willing to take risks can contribute significantly to creating innovative management approaches.

Part 8 – Theories of innovation strategy

While there is no single “main theory” of innovation management, the field is underpinned by several essential theories and concepts that form the basis of understanding. Here are some key elements:

  1. Innovation Ecosystem Theory: This theory posits that a firm’s innovation capacity is influenced by its connections within a larger ecosystem of stakeholders, including other businesses, government, and industry associations (Arenal et al., 2020; Asplund et al., 2021; Dodgson et al., 2013; Nylund et al., 2021). While this theory does not have a single specific creator, numerous scholars have developed and elaborated the idea in innovation studies over many years. It suggests that a firm’s innovation ability is shaped by its connections to a broader network or “ecosystem” of other firms, institutions, and stakeholders. In today’s interconnected global economy, this theory highlights the importance of strategic partnerships, collaborations, and industry alliances in driving innovation
  2. Organizational Culture Theory: This perspective suggests that organizational culture factors like psychological safety, collectivism, and power distance can significantly impact innovation performance. Psychological safety and collectivism generally positively impact innovation, while high power distance (a hierarchical culture) can have a negative effect (Kwantes & Boglarsky, 2007; Lee et al., 2019; Schneider et al., 2013). Likewise, this theory is the product of contributions from many scholars over time. It posits that the culture of an organization – its shared beliefs, values, and practices – can significantly impact the organization’s ability to innovate. In the modern business context, companies increasingly focus on fostering cultures that encourage creativity, risk-taking, and collaboration as critical drivers of innovation.
  3. Open Innovation Theory: This theory, proposed by Henry Chesbrough, suggests that companies can and should use internal and external theories and paths to market as they seek to advance their technology (de Jong et al., 2010; van de Vrande et al., 2010). Henry Chesbrough (2003) challenges the traditional notion of innovation being driven solely by internal R&D, instead suggesting that businesses should leverage internal and external ideas and pathways to advance their technology. Today, many companies use this approach, partnering with external researchers, customers, or even competitors to drive innovation.
  4. Diffusion of Innovations Theory: This theory, developed by Everett Rogers, describes how, over time, an idea or product gains momentum and diffuses (or spreads) through a particular population or social system (Rogers, 2010). Everett Rogers developed a theory to explain how innovations spread through populations over time. Businesses today use this theory to guide their marketing and adoption strategies, helping ensure that their creations reach as wide an audience as possible.
  5. Disruptive Innovation Theory: Proposed by Clayton Christensen, this theory suggests that a smaller company with fewer resources can successfully challenge established incumbent businesses by targeting segments of the market that have been neglected by the incumbents, typically because it is not profitable at the time (Christensen et al., 2006; Liversidge, 2015; Si & Chen, 2020). Clayton Christensen (2004) introduced a theory to describe how smaller, less-resourced companies can challenge established businesses by targeting neglected market segments. Today, this theory can be seen in many industries where start-ups have disrupted incumbents, such as Uber in transportation and Airbnb in hospitality.
  6. Resource-Based View (RBV): This theory posits that the competitive advantage of a firm lies primarily in the concentration of a bundle of valuable resources at the firm’s disposal (Barney & Arikan, 2005; Mele & Della Corte, 2013). Jay Barney and Birger Wernerfelt (Lazonick, 2002) posit that competitive advantage lies primarily in applying a bundle of valuable resources at a firm’s disposal. In today’s business, companies are more focused than ever on leveraging their unique resources and capabilities, whether proprietary technology, talented employees, or powerful brand identities, to innovate and achieve competitive advantage.

Innovation management is grounded in several essential theories and concepts that shape our understanding. Key elements include Innovation Ecosystem Theory (Arenal et al., 2020), which highlights the influence of a firm’s connections within a broader network of stakeholders on its innovation capacity (Oh et al., 2016). Organizational Culture Theory emphasizes how psychological safety and collectivism can impact innovation performance. Open Innovation Theory advocates leveraging internal and external ideas and pathways to advance technology. Diffusion of Innovations Theory explains how ideas or products spread through a population or social system. Disruptive Innovation Theory suggests that smaller companies can challenge incumbents by targeting neglected market segments. Finally, the Resource-Based View Theory focuses on leveraging valuable resources for competitive advantage (Barney & Arikan, 2005). Innovation management involves applying and combining these theories to foster new ideas while balancing existing operations and products.

Part 9 – Applying the theories to the introduction of blockchain and automation

Applying these theories is directly relevant to understanding the impact of new technologies and their transformational effects on business structures. Specifically, these theories can be used in the field of blockchain and automation (Dash et al., 2019), shedding light on the transitions that will occur in businesses due to these innovative technologies. Innovation Ecosystem Theory emphasizes that blockchain and automation technologies are not developed or implemented in isolation. Instead, they are part of a larger ecosystem that includes tech companies, financial institutions, regulatory bodies, and consumers. Consequently, the success of these technologies often hinges on effectively navigating and leveraging relationships within this ecosystem.

Organizational Culture Theory highlights the importance of cultivating a culture that encourages experimentation and tolerates failure in the context of blockchain and automation technologies. Given the novelty and complexity of these technologies, fostering a culture that embraces risk-taking and experimentation can attract top talent and accelerate innovation in these areas (Beaulieu & Reinstein, 2020).

Open Innovation Theory suggests that firms working with blockchain and automation technologies can benefit from partnering with external experts, such as academics, tech start-ups, and competitors. Collaborative efforts, such as joint research projects, data sharing, or co-development of new applications, can yield valuable insights and drive technological advancements. Diffusion of Innovations Theory (Rogers, 2010) recognizes that the widespread adoption of blockchain and automation depends on technical compatibility, perceived benefits, and cultural acceptance. Understanding these dynamics allows companies to effectively market these technologies and drive their endorsement and adoption within the industry.

Disruptive Innovation Theory highlights the potential for blockchain and automation to disrupt various industries by enabling new business models (Brintrup et al., 2020). For example, blockchain has the potential to revolutionize the financial sector by eliminating intermediaries, while automation can significantly impact manufacturing by reducing the need for human labor.

Resource-Based View (RBV) emphasizes leveraging available resources in blockchain and automation to gain a competitive advantage. For example, companies with significant resources in terms of technical expertise, intellectual property, or access to large datasets can leverage these advantages to develop proprietary blockchain algorithms or automation technologies that offer superior performance or functionality (Barney & Arikan, 2005).

In conclusion, these theories provide valuable perspectives for understanding the challenges and opportunities associated with successfully integrating new technologies into corporate structures, including blockchain and automation (Sandner et al., 2020). By utilizing these theories, companies can navigate the complex innovation landscape more effectively and position themselves for competitive advantage in the rapidly evolving technological landscape.


In conclusion, the field of innovation management is supported by various theories and concepts that provide valuable insights into the implementation and impact of new technologies, such as blockchain and automation, within businesses (Wang et al., 2018). The approaches discussed, including Innovation Ecosystem Theory, Organizational Culture Theory, Open Innovation Theory, Diffusion of Innovations Theory, Disruptive Innovation Theory, and Resource-Based View, offer lenses to understand the challenges and opportunities presented by these technologies.

By embracing an innovation ecosystem perspective, companies can navigate the intricate relationships and collaborations necessary to successfully implement blockchain and automation technologies. Cultivating an organizational culture that encourages experimentation, risk-taking, and tolerance for failure can foster an environment conducive to innovation in these fields. Open innovation (van de Vrande et al., 2010) approaches, including partnerships with external experts, can enhance the development and application of these technologies. Understanding the dynamics of technology diffusion and embracing disruptive innovation possibilities can guide businesses in effectively marketing and adopting blockchain and automation. Leveraging valuable resources, such as technical expertise or proprietary algorithms, can provide a competitive advantage in the rapidly evolving landscape.

By integrating these theories into their innovation management strategies, companies can better navigate the complexities of implementing new technologies, ensuring they are at the forefront of advancements in blockchain and automation technologies (Rehman Khan et al., 2022). In addition, the research and insights derived from these theories offer practical guidance for businesses seeking to leverage such technologies to enhance efficiency, competitiveness, and sustainable growth. As the field evolves, ongoing research and learning are necessary to stay abreast of emerging trends and refine innovation management practices. By embracing such theories and adapting to the changing technological landscape, organizations can position themselves for success in an increasingly innovative and dynamic business environment.


Anceaume, E., Ludinard, R., Potop-Butucaru, M., & Tronel, F. (2017). Bitcoin a distributed shared register. International Symposium on Stabilization, Safety, and Security of Distributed Systems, 456–468.

Arenal, A., Armuña, C., Feijoo, C., Ramos, S., Xu, Z., & Moreno, A. (2020). Innovation ecosystems theory revisited: The case of artificial intelligence in China. Telecommunications Policy44(6), 101960.

Asplund, F., Björk, J., Magnusson, M., & Patrick, A. J. (2021). The genesis of public-private innovation ecosystems: Bias and challenges✰. Technological Forecasting and Social Change162, 120378.

Attaran, M. (2020). Digital technology enablers and their implications for supply chain management. Supply Chain Forum: An International Journal21(3), 158–172.

Barney, J. B., & Arikan, A. M. (2005). The Resource-based View. In The Blackwell Handbook of Strategic Management (pp. 123–182). John Wiley & Sons, Ltd.

Beaulieu, P., & Reinstein, A. (2020). Connecting Organizational Culture to Fraud: Buffer/Conduit Theory. In K. E. Karim (Ed.), Advances in Accounting Behavioral Research (Vol. 23, pp. 21–45). Emerald Publishing Limited.

Berger, A. (1997). Continuous improvement and kaizen: Standardization and organizational designs. Integrated Manufacturing Systems8(2), 110–117.

Bhuiyan, N., & Baghel, A. (2005). An overview of continuous improvement: From the past to the present. Management Decision43(5), 761–771.

Børing, P. (2017). The relationship between training and innovation activities in enterprises: The relationship between training and innovation activities. International Journal of Training and Development21(2), 113–129.

Brintrup, A., Pak, J., Ratiney, D., Pearce, T., Wichmann, P., Woodall, P., & McFarlane, D. (2020). Supply chain data analytics for predicting supplier disruptions: A case study in complex asset manufacturing. International Journal of Production Research58(11), 3330–3341.

Cabrera, Á., Cabrera, E. F., & Barajas, S. (2001). The key role of organizational culture in a multi-system view of technology-driven change. International Journal of Information Management21(3), 245–261.

Çakar, N. D., & Ertürk, A. (2010). Comparing Innovation Capability of Small and Medium‐Sized Enterprises: Examining the Effects of Organizational Culture and Empowerment. Journal of Small Business Management48(3), 325–359.

Chesbrough, H. W. (2003). Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business Press.

Christensen, C. M., Anthony, S. D., & Roth, E. A. (2004). Seeing What’s Next: Using the Theories of Innovation to Predict Industry Change. Harvard Business Press.

Christensen, C. M., Baumann, H., Ruggles, R., & Sadtler, T. M. (2006). Disruptive innovation for social change. Harvard Business Review84(12), 94.

Cross, R., Gray, P., Cunningham, S., Showers, M., & Thomas, R. J. (2010). The Collaborative Organization: How to Make Employee Networks Really <em>Work</em>. MIT Sloan Management Review.

Curley, M., & Salmelin, B. (2017). Open innovation 2.0: The new mode of digital innovation for prosperity and sustainability. Springer.

Dash, R., McMurtrey, M., Rebman, C., & Kar, U. K. (2019). Application of Artificial Intelligence in Automation of Supply Chain Management. Journal of Strategic Innovation and Sustainability14(3), Article 3.

Davila, T., Epstein, M., & Shelton, R. (2012). Making Innovation Work: How to Manage It, Measure It, and Profit from It, Updated Edition. FT Press.

de Jong, J. P. J., Kalvet, T., & Vanhaverbeke, W. (2010). Exploring a theoretical framework to structure the public policy implications of open innovation. Technology Analysis & Strategic Management22(8), 877–896.

De Jong, J. P., Vanhaverbeke, W., Kalvet, T., & Chesbrough, H. (2008). Policies for open innovation: Theory, framework and cases. Tarmo Kalvet.

de Mast, J., & Lokkerbol, J. (2012). An analysis of the Six Sigma DMAIC method from the perspective of problem solving. International Journal of Production Economics139(2), 604–614.

Del Vecchio, P., Di Minin, A., Petruzzelli, A. M., Panniello, U., & Pirri, S. (2018). Big data for open innovation in SMEs and large corporations: Trends, opportunities, and challenges. Creativity and Innovation Management27(1), 6–22.

Dodgson, M., Gann, D. M., & Phillips, N. (2013). The Oxford Handbook of Innovation Management. OUP Oxford.

Dombrowski, C., Kim, J. Y., Desouza, K. C., Braganza, A., Papagari, S., Baloh, P., & Jha, S. (2007). Elements of innovative cultures. Knowledge and Process Management14(3), 190–202.

Fernandes, A. J., & Ferreira, J. J. (2022). Entrepreneurial ecosystems and networks: A literature review and research agenda. Review of Managerial Science16(1), 189–247.

Forcadell, F. J., & Guadamillas, F. (2002). A case study on the implementation of a knowledge management strategy oriented to innovation. Knowledge and Process Management9(3), 162–171.

George, G., McGahan, A. M., & Prabhu, J. (2012). Innovation for Inclusive Growth: Towards a Theoretical Framework and a Research Agenda: Innovation for Inclusive Growth. Journal of Management Studies49(4), 661–683.

Gephart, M. A., Marsick, V. J., Buren, M. E. V., Spiro, M. S., & Senge, P. (1996). Learning organizations come alive. Training &amp; Development50(12), 34–46.

Henao-García, E. A., & Cardona Montoya, R. A. (2023). Management innovation and its relation to innovation outcomes and firm performance: A systematic literature review and future research agenda. European Journal of Innovation Managementahead-of-print(ahead-of-print).

Ho, W. R., Tsolakis, N., Dawes, T., Dora, M., & Kumar, M. (2022). A Digital Strategy Development Framework for Supply Chains. IEEE Transactions on Engineering Management, 1–14.

Kaplan, R. S. (1998). Innovation action research: Creating new management theory and practice. Journal of Management Accounting Research10, 89.

Kumar, A., Liu, R., & Shan, Z. (2020). Is Blockchain a Silver Bullet for Supply Chain Management? Technical Challenges and Research Opportunities. Decision Sciences51(1), 8–37.

Kumar, N. (Ed.). (2020). Blockchain, big data and machine learning: Trends and applications (First edition). CRC Press.

Kumar, V., & Raheja, G. (2012). Business to business (B2B) and business to consumer (B2C) management. In

Kwantes, C. T., & Boglarsky, C. A. (2007). Perceptions of organizational culture, leadership effectiveness and personal effectiveness across six countries. Journal of International Management13(2), 204–230.

Law, A. (2017). Smart contracts and their application in supply chain management [Thesis, Massachusetts Institute of Technology].

Lazonick, W. (2002). Innovative Enterprise and Historical Transformation. Enterprise & Society3(1), 3–47.

Lee, Y., Howe, M., & Kreiser, P. M. (2019). Organisational culture and entrepreneurial orientation: An orthogonal perspective of individualism and collectivism. International Small Business Journal37(2), 125–152.

Liversidge, G. (2015). Christensen’s Disruptive Innovation and Schumpeter’s Creative Destruction.

Martins, E. C., & Terblanche, F. (2003). Building organisational culture that stimulates creativity and innovation. European Journal of Innovation Management6(1), 64–74.

Mele, C., & Della Corte, V. (2013). Resource-Based View and Service-Dominant Logic: Similarities, Differences and Further Research (SSRN Scholarly Paper No. 2488529).

Mendling, J., Weber, I., Aalst, W. V. D., Brocke, J. V., Cabanillas, C., Daniel, F., Debois, S., Ciccio, C. D., Dumas, M., Dustdar, S., Gal, A., García-Bañuelos, L., Governatori, G., Hull, R., Rosa, M. L., Leopold, H., Leymann, F., Recker, J., Reichert, M., … Zhu, L. (2018). Blockchains for Business Process Management—Challenges and Opportunities. ACM Transactions on Management Information Systems9(1), 1–16.

Mohr, J. J., & Sarin, S. (2009). Drucker’s insights on market orientation and innovation: Implications for emerging areas in high-technology marketing. Journal of the Academy of Marketing Science37(1), 85–96.

Mumford, M. D., Scott, G. M., Gaddis, B., & Strange, J. M. (2002). Leading creative people: Orchestrating expertise and relationships. The Leadership Quarterly13(6), 705–750.

Nagji, B., & Tuff, G. (2012). Managing your innovation portfolio. Harvard Business Review90(5), 66–74.

Nylund, P. A., Brem, A., & Agarwal, N. (2021). Innovation ecosystems for meeting sustainable development goals: The evolving roles of multinational enterprises. Journal of Cleaner Production281, 125329.

Oh, D.-S., Phillips, F., Park, S., & Lee, E. (2016). Innovation ecosystems: A critical examination. Technovation54, 1–6.

O’Sullivan, D., & Dooley, L. (2008). Applying Innovation. SAGE Publications.

Papadonikolaki, E., Tezel, A., Yitmen, I., & Hilletofth, P. (2022). Blockchain innovation ecosystems orchestration in construction. Industrial Management & Data Systems123(2), 672–694.

Prayuda, R. Z. (2020). Continuous Improvement Through Kaizen In An Automotive Industry. Journal of Industrial Engineering & Management Research1(1b), Article 1b.

Rehman Khan, S. A., Ahmad, Z., Sheikh, A. A., & Yu, Z. (2022). Digital transformation, smart technologies, and eco-innovation are paving the way toward sustainable supply chain performance. Science Progress105(4), 003685042211456.

Rogers, E. M. (2010). Diffusion of Innovations, 4th Edition. Simon and Schuster.

Sáez, G., & Inmaculada, M. (2020). Blockchain-Enabled Platforms: Challenges and Recommendations.

Sandner, P., Lange, A., & Schulden, P. (2020). The Role of the CFO of an Industrial Company: An Analysis of the Impact of Blockchain Technology. Future Internet12(8), Article 8.

Sarkodie, S. A., & Owusu, P. A. (2022). Dataset on bitcoin carbon footprint and energy consumption. Data in Brief42, 108252.

Schmidt, A. L., & Van Der Sijde, P. (2022). Disruption by design? Classification framework for the archetypes of disruptive business models. R&D Management52(5), 893–929.

Schneider, B., Ehrhart, M. G., & Macey, W. H. (2013). Organizational Climate and Culture. Annual Review of Psychology64(1), 361–388.

Si, S., & Chen, H. (2020). A literature review of disruptive innovation: What it is, how it works and where it goes. Journal of Engineering and Technology Management56, 101568.

Sklaroff, J. M. (2017). Smart Contracts and the Cost of Inflexibility Comment. University of Pennsylvania Law Review166(1), [i]-304.

Tamayo-Orbegozo, U., Vicente-Molina, M.-A., & Villarreal-Larrinaga, O. (2017). Eco-innovation strategic model. A multiple-case study from a highly eco-innovative European region. Journal of Cleaner Production142, 1347–1367.

Teece, D. J. (2010). Business Models, Business Strategy and Innovation. Long Range Planning43(2), 172–194.

Teece, D. J. (2019). A capability theory of the firm: An economics and (Strategic) management perspective. New Zealand Economic Papers53(1), 1–43.

Tiwari, S. P. (2022). Organizational Competitiveness and Digital Governance Challenges. SSRN Electronic Journal.

van de Vrande, V., Vanhaverbeke, W., & Gassmann, O. (2010). Broadening the scope of open innovation: Past research, current state and future directions. International Journal of Technology Management52(3/4), 221–235.

VanStelle, S. E., Vicars, S. M., Harr, V., Miguel, C. F., Koerber, J. L., Kazbour, R., & Austin, J. (2012). The Publication History of the Journal of Organizational Behavior Management: An Objective Review and Analysis: 1998–2009. Journal of Organizational Behavior Management32(2), 93–123.

Wang, Y., Han, J. H., & Beynon-Davies, P. (2018). Understanding blockchain technology for future supply chains: A systematic literature review and research agenda. Supply Chain Management: An International Journal24(1), 62–84.

Wang, Y., Singgih, M., Wang, J., & Rit, M. (2019). Making sense of blockchain technology: How will it transform supply chains? International Journal of Production Economics211, 221–236.

Youtie, J., Ward, R., Shapira, P., Schillo, R. S., & Louise Earl, E. (2023). Exploring New approaches to understanding innovation ecosystems. Technology Analysis & Strategic Management35(3), 255–269.

Yukl, G. (2008). How leaders influence organizational effectiveness. The Leadership Quarterly19(6), 708–722.

Zahra, S. A., & Nambisan, S. (2012). Entrepreneurship and strategic thinking in business ecosystems. Business Horizons55(3), 219–229.

Zhang, W., Zeng, X., Liang, H., Xue, Y., & Cao, X. (2023). Understanding How Organizational Culture Affects Innovation Performance: A Management Context Perspective. Sustainability15(8), Article 8.

[1] The material I am documenting extends the PowerPoint and aids in understanding the material presented.

London Blockchain Conference Day 1 Highlights: Revenue generation with blockchain tech

YouTube video

New to blockchain? Check out CoinGeek’s Blockchain for Beginners section, the ultimate resource guide to learn more about blockchain technology.