Just as we would be remiss to skip past a discussion of the role of entrepreneurs in innovation, we would be remiss to skip over the role of the state. In this chapter, we move through three starkly different visions of what role government ought to play in bringing about innovation in the economy. The first paper discussed, by Acemoglu & Robinson, suggests that the state actually plays a key role in creating the institutions that make innovation worthwhile. The second reading, a set of chapters from a book by Mazzucato, argues that this institution-oriented view is too limited, provides evidence of how ‘entrepreneurial states’ can also work to develop innovations, and suggests that this implies a state that is much more active in investing in and directing innovation. The third reading turns up this argument further, arguing that the urgency of the global climate crisis and the vast economic reorganization that it demands means that the state should not just be more active in investing and directing: the crisis, it argues, can only be solved by a complete socialization of the economy, by the state actively managing innovation and production.

Even as a romantic conception of innovation – emphasizing its uncertain and serendipitous nature, for instance – might suggest that it is inherently hard to manage, the brute fact of the matter is that most innovation, in most organizations, is managed. In this chapter, we look into what happens to innovation when it is subjected to management, paying particular attention to the unintended and second-order consequences of those efforts to manage. Management can surely “get things under control,” but the interesting questions relate to what happens next, to what also happens when things do get under control. The first three readings provide three different angles on that. In the first, we read about a pretty neutral-looking management technique and think through why it might not be so neutral. In the second, we are shown how innocuous things like accounting numbers can drive innovation strategies. In the third, we are introduced to the dynamics of hidden innovation projects and think about what formal management actually gets to manage and the limits of managerial influence. The final reading zooms out and asks what happens when organizations actually lean into the unmanageability of innovation and attempt to be less organized and to manage innovation less.

In the book’s first chapter, we looked at how technological change created conditions for the emergence and dominance of a specific form of innovation management. Coming full circle, this chapter asks how further technological change might challenge that form. We begin with technological change that is already here and that has begun to impact how innovation is managed. In Baldwin and von Hippel’s analysis, innovations in communication technologies and design tools are given credit for the expanding role of nonfirms in innovation, effecting shifts in the locus of innovation in society away from producer-firms toward users and peer-to-peer collaborations. In that view, firms might be poised to play a much smaller role in the innovation process of the future. Examining some of the same technologies, Altman et al. do not see the imminent end of firm-centric innovation. Instead, they propose that innovation management will evolve, that firms will take on new forms of collaboration and more porous boundaries to fully benefit from the possibilities of technological change. To close, we turn again to the question of how artificial intelligence might play into the further transformation of organizations. Cockburn et al.’s analysis asks whether artificial intelligence might be a method in the method of invention and what that might mean for the future of innovation and economic development.

Technology is often a crucial input to innovation, but so are knowledge and ideas. Indeed, technological change emerges out of ideas and, as we will see, knowledge stands in an interesting relationship to the generation of the novel ideas that you could say are essential raw materials for innovation. First, Galenson’s work provides some nuance to the nature of ideas and of knowledge, suggesting that knowledge can be both a prerequisite for and an impediment to the development of ideas, depending crucially on what kind of ideas we are dealing with. Then, the papers by Ward and Shane present two contrasting views of how knowledge can shape imagination, highlighting how one’s prior knowledge might overstructure, enable, or narrowly focus ideation. Finally, Jones’ paper zooms out to the macro level, inviting us to think about why it appears to be getting harder and harder to have ideas early in life.

In this chapter, we lay out the basic frame for studying innovation management. To do so, we are going to try to understand why innovation is important for society, for companies, and for individuals, and to do that we take our point of departure in the “urtext” of innovation research, namely Schumpeter’s work on Capitalism, Socialism and Democracy and especially the notion of Creative Destruction. To follow that up, we are going to untangle how innovation management fits within a broader context of capitalism as an economic system, within a particular ideology, and within the operations of the modern corporation.

What is the value of theory for management practice? Recent scholarship suggests that managers who work like scientists perform better along a number of dimensions. Mastering a broad repertoire of theory, being able to apply that repertoire to make better sense of organizations and innovation, and being able to think through the limitations and possibilities of theory are what allows innovation managers to work like scientists.

In this chapter, we are going to focus on technology and how technological change impacts industries. The central metaphor for understanding technological development is the technology “S-curve.” The central way to understand new technologies’ impact on industries is as cyclical, as short periods of rapid and dramatic change followed by long periods of incremental improvement. We will consider both and explore how they connect to one another, to the dynamics of Creative Destruction, to the dominant organization of industrial firms, and to the balance required between exploration and exploitation. With these basics in place, we are going to complicate the relatively straightforward story they produce. We are going to examine how technological performance at the system level is a function of sometimes highly unpredictable changes at the subcomponent level and sometimes of changes at the ecosystem level. Both can render predictions of technological change and competitive dynamics more difficult and open up new avenues for innovation management action.

In this chapter, we are going to look at different forms of innovation, but not along the typical axes of difference. In the previous three chapters, we have distinguished between innovation in products and services, processes, and organizational practices, and between innovations that are mostly exploitative and incremental and those that are explorative and radical. The ones we will cover in this chapter are characterized by additional, complicating features that lead them to have different opportunities for new entrants, different challenges for incumbents, and different effects on industries. These features include particular aspects of marketing, organization, and uncertainty. We are going to close the chapter by taking a slightly different angle on the relationship between technology and industries. Where we have so far taken that relationship as given, we are going to look at how technologies that are potentially general in nature get applied to several different industries through a process of opportunity discovery that can in no reasonable way be taken for granted and what that might imply for technological improvement.

In this chapter, we are going to look at how organizations can combine exploitative and explorative learning. If they can it would after all challenge some of the theoretical preconceptions that Abernathy and March would give us and raise serious questions about whether Creative Destruction is in fact the inevitable threat that Schumpeter made it out to be. As it turns out, there are multiple “recipes” for how to effectively combine exploration and exploitation, based on a variety of empirical observations, that might make us less confident in the generalizability of the Productivity Dilemma. Zooming in on Communities of Practice, we are also invited to think through whether at some levels of the organization the distinction between exploration and exploitation even makes sense while Cohen and Levinthal’s work invites us to reconsider whether it is actually a problem that firms specialize in either exploration or exploitation. After all, they could always collaborate with other organizations that do well what they themselves do not.