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Designing innovation networks
Chances are your organization has some people who are passionate about innovation and others who feel uncomfortable about any topic related to change. Recent academic research finds that differences in individual creativity and intelligence matter far less for innovation than connections and networks-for example, networked employees can realize their innovations and make them catch on more quickly.7
Since new ideas seem to spur more new ideas, networks generate a cycle of innovation. Furthermore, effective networks allow people with different kinds of knowledge and ways of tackling problems to cross-fertilize ideas. By focusing on getting the most from innovation networks, leaders can therefore capture more value from existing resources, without launching a large-scale change-management program.
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Social-network analysis can help executives to diagnose existing networks in order to ascertain their characteristics, such as the frequency of collaboration and the degree of cross-functional interactions among members, and to identify people who broker information and knowledge. This kind of information can also serve an essential role in the creation of effective innovation networks by clarifying the mind-sets of individuals and groups.
In one company, for example, we found three groups with distinct perspectives on innovation. One believed that the company was innovative, but the other two, with 57 percent of its employees, thought that it wasn't-indeed, that it was actually bureaucratic, slow moving, inefficient, and stressful. A separately developed network map highlighted the company's hierarchical structure but also showed that cross-functional departments were well connected.
When we combined the analysis of personal perspectives on innovation with the network map, we found opportunities for improvement. Paradoxically, the analysis revealed that those employees, largely middle managers, with the most negative attitude toward innovation were also the most highly sought after for advice about it. In effect, they served as bottlenecks to the flow of new ideas and the open sharing of knowledge. A further analysis of the people in this group highlighted their inability to balance new ideas with current priorities and to behave as leaders rather than supervisors. We have observed that middle managers pose similar challenges in many organizations.
Senior management used this analysis to create a network of middle managers who were encouraged to generate newer and bigger ideas. Members of the network regularly discussed new ideas with senior executives, and these ideas were evaluated collectively by mutually agreed-upon criteria.
Shaping innovation networks is both an art and a science. Any network is unpredictable and, in the end, impossible to control. Focusing on the replacement of one or two ineffective members has less impact than establishing the conditions for vibrant networks and taking advantage of the connections through which they flourish.
Making networks more decentralized is another way to improve collaboration and performance (Exhibit 1). Consider the case of two geographically separate units that undertake the same activities. A larger leadership group with an open and positive mind-set is a distinguishing feature of the higher-performing unit. Its information network is also more decentralized, with a larger number of connections. Hierarchy is still evident in the higher-performing unit, but its information and knowledge network is more distributed, and more of the members participate actively. The lower-performing unit has just one leader, who controls most of the interactions and has a negative mind-set about openness and collaboration, and there are far fewer connections. The network design is more centralized.
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The four critical steps in designing, implementing, and managing an innovation network are presented in Exhibit 2. In addition, executives can fine-tune the network's goals by identifying the appropriate mix and balance of employees. Innovation networks, like cross-functional teams, require different skills and attitudes. In our experience, they include combinations of several archetypes:
- Idea generators prefer to come up with ideas, believe that asking the right questions is more important than having the right answers, and are willing to take risks on high-profile experiments.
- Researchers mine data to find patterns, which they use as a source of new ideas. They are the most likely members of the network to seek consumer insights and to regard such insights as a primary input.
- Experts value proficiency in a single domain and relish opportunities to get things done.
- Producers orchestrate the activities of the network. Others come to them for new ideas or to get things done. Producers are also the most likely members of the network to be making connections across teams and groups.
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This kind of staffing is clearly an inexact science. A team or network in need of more ideas might get additional idea generators to fill the gap. If the challenge is commercializing the right ideas, management might opt to add producers and experts. In our survey of professionals, respondents who regarded their companies as more innovative than competitors in the same industry were also more likely to work for companies that had larger numbers of producers.
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