Accelerating IDT (Industrial Technology Adoption) in UK manufacturing through Open Innovation
If solutions involving Industrial Digital Technologies (IDTs) that meet the needs of UK manufacturers are readily available, they can be obtained through market-based contracts or licensing agreements. However, some manufacturers may have more complex requirements that necessitate highly customized solutions or even the development of new technologies. In these situations, manufacturers and IDT providers may need to engage in Open Innovation.
Open Innovation (OI) means working with external partners to find solutions to innovation-related problems. It is a distributed innovation process based on purposively managed knowledge flows across organizational boundaries.
Both systematic research and anecdotal evidence show that OI has many benefits, such as:
OI Mechanisms
Once firms have decided to embrace OI, the choice of OI mechanism is the next important decision for successful OI management. Researchers have identified two main OI mechanisms to tap into outsider knowledge:
Cooperative interorganizational relationships within networks of external partners, such as universities, suppliers, and competitors (e.g., alliances, joint ventures).
Openly broadcasting (often through OI intermediaries) an organizational problem to a large pool of potentially interested people, out of which some self-select to participate in the development of innovative solutions.
Each OI mechanism has different benefits (e.g., accessing a broad range of potential solutions, providing a rich communication channel between solution seekers and partners) and costs (e.g., coordination and opportunism costs; setup costs). Even within the same organization, different innovation projects should be managed through different OI mechanisms.
Decision-Making Framework
Based on our research, we propose a decision-making framework for selecting appropriate OI mechanisms, which involves matching the project attributes with the features of each OI mechanism to maximise benefits and minimise costs.
Our decision framework includes two phases.
Phase One:
A Function of Complexity
The first phase aims to match the project's complexity level with the two primary OI mechanisms: crowdsourcing and partnerships. Complexity is defined by the number of essential elements (such as knowledge sets and components) required to complete an innovation project, as well as the interdependencies between these elements. Complex projects involve numerous highly interdependent elements that significantly affect the final solution. As complexity increases, these interdependencies become poorly understood, unexpected, or unknown, making it challenging to find an optimal solution since the performance of one element can influence the performance of others.
For complex projects, companies (e.g., manufacturers and IDT providers) must choose an OI mechanism that considers these interdependencies and fosters knowledge sharing through deep interaction and communication. Partnerships (e.g., alliances, joint ventures) offer rich communication channels that allow the solution-seeking organization to engage closely with external partners, making them more suitable for complex projects than crowdsourcing, which lacks such in-depth communication capabilities.
Finding an optimal solution for simple or less complex projects is less challenging. This is because the elements of these projects are not highly interdependent, and the project can be broken down into distinct, independent components, reducing the need for extensive communication and knowledge sharing. For simpler projects, crowdsourcing is a suitable OI mechanism, enabling solution seekers to tap into a diverse range of otherwise hidden knowledge. This approach can lead to novel and unconventional solutions, especially from contributors whose expertise might not be directly related to the innovation project.
Project with specific attributes
Simple project Complex project
Phase Two:
The Appropriate Types of Partnerships and Crowdsourcing
Partnerships: Locating Relevant Knowledge
The second phase involves selecting the right type of partnership or crowdsourcing, again by aligning the attribute of the innovation project with the features of the OI mechanisms.
Our research indicates that both non-equity and equity partnerships are suitable OI mechanisms for managing complex projects. In a non-equity partnership (e.g., alliances with suppliers), solution seekers collaborate with external partners without involving equity transactions, whereas an equitybased partnership (e.g., mergers, acquisitions, joint ventures, minority holdings) involves equity transactions. Non-equity partnerships offer access to a broader range of potential partners and are less costly to establish, as they do not require significant initial capital investments. However, equity partnerships can facilitate more effective communication channels for knowledge sharing and involve lower coordination and opportunism costs due to equity control through a joint board.
When solution seekers encounter complex problems and can identify partners with relevant expertise, non-equity partnerships are often more appropriate than equity-based ones. This is because projects can be divided according to the specializations of the partners, allowing solution seekers to leverage the knowledge of a larger group of external collaborators, thereby reducing project costs and time-to-market. Additionally, non-equity partnerships do not require the heavy initial investment associated with equity-based partnerships, which involve substantial setup costs.
Conversely, when the relevant knowledge is unknown to solution seekers, decomposing the project (a crucial step for maximizing the benefits of non-equity partnerships) becomes impractical. Furthermore, if the required knowledge is unavailable, non-equity partnerships may incur significant coordination and opportunism costs due to challenges in drafting a comprehensive contract. Therefore, for highly complex projects where the necessary knowledge is either unavailable or hidden, forming equity-based partnerships with elite players (within the same industry or across different industries) is the more suitable OI mechanism, as these partnerships provide effective communication channels and reduce coordination efforts.
Crowdsourcing: Accounting for the Pervasiveness of Relevant Knowledge
There are two main types of crowdsourcing that can be leveraged for innovation purposes:
The typical type known as “fishing,” where solution-seekers launch an open call (potentially through OI intermediaries) to invite the participation of solution providers, and then wait for them to self-select and share their solutions, from which solution seekers select the best one.
The more recent type known as “hunting,” where solution seekers engage in a more proactive approach by identifying relevant solution providers. Vast amounts of data, including patent and academic paper databases, are scoured by leveraging data mining and machine learning to identify expert solution providers who hold relevant knowledge and have higher chances to develop appropriate solutions. These experts are subsequently contacted directly and invited to share their solutions, instead of waiting for them to self-select and respond to an open call.
The ability of a crowd member to solve an innovation-related problem depends on how widespread the necessary problem-solving knowledge is within the crowd. When the required knowledge is less common, individuals with the relevant expertise become scarce in the general population. These specialists are often in high demand, making them less likely to actively seek out open calls, which is essential for the “fishing” approach to crowdsourcing. Consequently, “fishing” is less effective in attracting top solution providers for projects that require specialized knowledge not widely available in the crowd. In contrast, the “hunting” approach to crowdsourcing allows solution seekers to directly identify and engage with qualified experts who possess rare, specialized knowledge. On the other hand, “fishing” can be very effective for projects that require generalist knowledge, which is more widespread within the crowd. In such cases, many people are likely to have the necessary expertise and the motivation to actively search for open calls. Crowdsourcing Low
To find out more about the research: Core references:
Bagherzadeh, M. and Gurca, A. (2024) Open innovation: aligning mechanisms with project attributes, The Oxford handbook of open innovation. Oxford University Press.
Bagherzadeh, M., Gurca, A., and Velayati, R. (2023). Crowdsourcing routines: the behavioral and motivational underpinnings of expert participation. Industrial and Corporate Change, 32(6), 1393-1409.
Bagherzadeh, M., Gurca, A. and Brunswicker, S. (2019) Problem types and open innovation governance modes: A project-level empirical exploration, IEEE Transactions on Engineering Management.
