Innovation Theory

Metrology plays a key role in the manufacture of mechanical components. Traditionally it is used extensively in a pre-process stage where a manufacturer does process planning, design, and ramp-up, and in post-process off-line inspection to establish proof of quality. The area that is seeing a lot of growth is the in-process stage of volume manufacturing, where feedback control can help ensure that parts are made to specification. The Industrial Metrology Group at Carl Zeiss AG had its traditional strength in high precision coordinate measuring machines, a universal measuring tool that had been widely used since its introduction in the mid-1970s. The market faced a complex diversification of competition as metrology manufacturers introduced new sensor and measurement technologies, and as some of their customers moved towards a different style of measurement mandating speed and integration with production systems. The case discusses the threat of new in-line metrology systems to the core business as well as the arising new opportunities.

The purpose of this article is to suggest a (preliminary) taxonomy and research agenda for the topic of “firms, crowds, and innovation” and to provide an introduction to the associated special issue. We specifically discuss how various crowd-related phenomena and practices—for example, crowdsourcing, crowdfunding, user innovation, and peer production—relate to theories of the firm, with particular attention on “sociality” in firms and markets. We first briefly review extant theories of the firm and then discuss three theoretical aspects of sociality related to crowds in the context of strategy, organizations, and innovation: (1) the functions of sociality (sociality as extension of rationality, sociality as sensing and signaling, sociality as matching and identity); (2) the forms of sociality (independent/aggregate and interacting/emergent forms of sociality); and (3) the failures of sociality (misattribution and misapplication). We conclude with an outline of future research directions and introduce the special issue papers and essays.

In this paper, I study the effect of adding large numbers of producers of application software programs (“apps”) to leading handheld computer platforms, from 1999 to 2004. To isolate causal effects, I exploit changes in the software labor market. Consistent with past theory, I find a tight link between the number of producers on platform and the number of software varieties that were generated. The patterns indicate the link is closely related to the diversity and distinct specializations of producers. Also highlighting the role of heterogeneity and nonrandom entry and sorting, later cohorts generated less compelling software than earlier cohorts. Adding producers to a platform also shaped investment incentives in ways that were consistent with a tension between network effects and competitive crowding, alternately increasing or decreasing innovation incentives depending on whether apps were differentiated or close substitutes. The crowding of similar apps dominated in this case; the average effect of adding producers on innovation incentives was negative. Overall, adding large numbers of producers led innovation to become more dependent on population-level diversity, variation, and experimentation —while drawing less on the heroic efforts of any one individual innovator.

Innovation requires sources of novelty, but the challenge is that not all sources lead to innovation, so its value needs to be determined. However, since ways of determining value stem from existing knowledge, this often creates barriers to innovation. To understand how people address the challenge of novelty, we develop a conceptual and an empirical framework to explain how this challenge is addressed in a software and scientific context. What is shown is that the process of innovation is a cycle where actors develop a novel course of action and, based on the consequences identified, confirm what knowledge is necessary to transform and develop the next course of action. The performance of the process of innovation is constrained by the capacities of the artifacts and the ability of the actors to create and use artifacts to drive this cycle. By focusing on the challenge of novelty, a problem that cuts across all contexts of innovation, our goal is to develop a more generalized account of what drives the process of innovation.

Most of society's innovation systems – academic science, the patent system, open source, etc. – are “open” in the sense that they are designed to facilitate knowledge disclosure among innovators. An essential difference across innovation systems is whether disclosure is of intermediate progress and solutions or of completed innovations. We theorize and present experimental evidence linking intermediate versus final disclosure to an ‘incentives-versus-reuse’ tradeoff and to a transformation of the innovation search process. We find intermediate disclosure has the advantage of efficiently steering development towards improving existing solution approaches, but also has the effect of limiting experimentation and narrowing technological search. We discuss the comparative advantages of intermediate versus final disclosure policies in fostering innovation.

This paper studies two fundamentally distinct approaches to opening a technology platform and their different impacts on innovation. One approach is to grant access to a platform and thereby open up markets for complementary components around the platform. Another approach is to give up control over the platform itself. Using data on 21 handheld computing systems (1990–2004), I find that granting greater levels of access to independent hardware developer firms produces up to a fivefold acceleration in the rate of new handheld device development, depending on the precise degree of access and how this policy was implemented. Where operating system platform owners went further to give up control (beyond just granting access to their plat- forms) the incremental effect on new device development was still positive but an order of magnitude smaller. The evidence from the industry and theoretical arguments both suggest that distinct economic mechanisms were set in motion by these two approaches to opening.

Teaching Note for HBS Case 610-032.

TopCoder's crowdsourcing-based business model, in which software is developed through online tournaments, is presented. The case highlights how TopCoder has created a unique two-sided innovation platform consisting of a global community of over 225,000 developers who compete to write software modules for its over 40 clients. Provides details of a unique innovation platform where complex software is developed through ongoing online competitions. By outlining the company's evolution, the challenges of building a community and refining a web-based competition platform are illustrated. Experiences and perspectives from TopCoder community members and clients help show what it means to work from within or in cooperation with an online community. In the case, the use of distributed innovation and its potential merits as a corporate problem solving mechanism is discussed. Issues related to TopCoder's scalability, profitability, and growth are also explored.

This paper discusses several challenges in designing field experiments to better understand how organizational and institutional design shapes innovation outcomes and the production of knowledge. We proceed to describe the field experimental research program carried out by our Crowd Innovation Laboratory at Harvard University to clarify how we have attempted to address these research design challenges. This program has simultaneously solved important practical innovation problems for partner organizations, like NASA and Harvard Medical School (HMS), while contributing research advances, particularly in relation to innovation contests and tournaments. We conclude by proceeding to highlight the opportunity for the scholarly community to develop a “science of innovation” that utilized field experiments as means to generate knowledge.

This chapter reports on an actual field experiment that tests for the influence of “sorting” on innovator effort. The focus is on the potential heterogeneity among innovators and whether they prefer a more cooperative versus competitive research environment. The focus of the field experiment is a real-world multiday software coding exercise in which participants are able to express a preference for being sorted into a cooperative or competitive environment—that is, incentives in the cooperative environment are team based, while those in the competitive environment are individualized and depend on relative performance. Half of the participants are indeed sorted on the basis of their preferences, while the other half are assigned to the two modes on a random basis.

We examine who the winners are in science problem-solving contests characterized by open broadcast of problem information, self-selection of external solvers to discrete problems from the laboratories of large R&D intensive companies, and blind review of solution submissions. We find that technical and social marginality, being a source of different perspectives and heuristics, plays an important role in explaining individual success in problem solving. The provision of a winning solution was positively related to increasing distance between the solver's field of technical expertise and the focal field of the problem. Female solvers—known to be in the "outer circle" of the scientific establishment—performed significantly better than men in developing successful solutions. Our findings contribute to the emerging literature on open and distributed innovation by demonstrating the value of openness, at least narrowly defined by disclosing problems, in removing barriers to entry to non-obvious individuals. We also contribute to the knowledge-based theory of the firm by showing the effectiveness of a market mechanism to draw out knowledge from diverse external sources to solve internal problems.

This chapter contrasts traditional, organization- centered models of innovation with more recent work on open innovation. These fundamentally different and inconsistent innovation logics are associated with contrasting organizational boundaries and organizational designs. We suggest that when critical tasks can be modularized and when problem- solving knowledge is widely distributed and available, open innovation complements traditional innovation logics. We induce these ideas from the literature and with extended examples from Apple, the National Aeronautics and Astronomical Agency (NASA) and LEGO. We suggest that task decomposition and problem- solving knowledge distribution are not deterministic but are strategic choices. If dynamic capabilities are associated with innovation streams, and if different innovation types are rooted in contrasting innovation logics, there are important implications for firm boundaries, design and identity.

This paper provides a basic conceptual framework for interpreting non-price instruments used by multi-sided platforms (MSPs) by analogizing MSPs as "private regulators" who regulate access to and interactions around the platform. We present evidence on Facebook, TopCoder, Roppongi Hills and Harvard Business School to document the "regulatory" role played by MSPs. We find MSPs use nuanced combinations of legal, technological, informational and other instruments (including price-setting) to implement desired outcomes. Non-price instruments were very much at the core of MSP strategies.

This note outlines the structure and content of a seven-session module that is designed to introduce students to the fundamentals of innovating with the "crowd." The module has been taught in a second year elective course at the Harvard Business School on "Digital Innovation and Transformation" and is aimed at students that already have an understanding of how to structure an innovation process inside of a company. The module expands the students' innovation toolkit by exposing them to the theory and practice of extending the innovation process to external participants.

The last two decades have witnessed an extraordinary growth of new models of managing and organizing the innovation process, which emphasize users over producers. Large parts of the knowledge economy now routinely rely on users, communities, and open innovation approaches to solve important technological and organizational problems. This view of innovation, pioneered by the economist Eric von Hippel, counters the dominant paradigm, which casts the profit-seeking incentives of firms as the main driver of technical change. In a series of influential writings, von Hippel and colleagues found empirical evidence that flatly contradicted the producer-centered model of innovation. Since then, the study of user-driven innovation has continued and expanded, with further empirical exploration of a distributed model of innovation that includes communities and platforms in a variety of contexts and with the development of theory to explain the economic underpinnings of this still emerging paradigm. This volume provides a comprehensive and multidisciplinary view of the field of user and open innovation, reflecting advances in the field over the last several decades.

The contributors—including many colleagues of Eric von Hippel—offer both theoretical and empirical perspectives from such diverse fields as economics, the history of science and technology, law, management, and policy. The empirical contexts for their studies range from household goods to financial services. After discussing the fundamentals of user innovation, the contributors cover communities and innovation; legal aspects of user and community innovation; new roles for user innovators; user interactions with firms; and user innovation in practice, describing experiments, toolkits, and crowdsourcing and crowdfunding.

Contests are a historically important and increasingly popular mechanism for encouraging innovation. A central concern in designing innovation contests is how many competitors to admit. Using a unique data set of 9,661 software contests, we provide evidence of two coexisting and opposing forces that operate when the number of competitors increases. Greater rivalry reduces the incentives of all competitors in a contest to exert effort and make investments. At the same time, adding competitors increases the likelihood that at least one competitor will find an extreme-value solution. We show that the effort-reducing effect of greater rivalry dominates for less uncertain problems, whereas the effect on the extreme value prevails for more uncertain problems. Adding competitors thus systematically increases overall contest performance for high-uncertainty problems. We also find that higher uncertainty reduces the negative effect of added competitors on incentives. Thus, uncertainty and the nature of the problem should be explicitly considered in the design of innovation tournaments. We explore the implications of our findings for the theory and practice of innovation contests.