Andrea Blasco, Kevin Boudreau, Karim R. Lakhani, Michael Menietti, and Christoph Riedl. 2013. “
Do Crowds Have the Wisdom to Self-Organize?”.
AbstractThe “self-organizing” of online crowds — or workers, more generally — into teams is a non-trivial problem of coordination and matching, in a context in which other parties are simultaneously competing for partners. Here, we experimentally investigate the capacity for workers in online crowds to self-organize into teams, within a scientific crowdsourcing contest. We compare matching outcomes and performance to those in a comparison group in which we eliminate the coordination and matching problem altogether (by directly assigning individuals to Pareto efficient teams). Online crowd members do remarkably well relative to the benchmark achieving 13% more functioning teams. Teams also tended to be more effective, by several measures. (We found no evidence these levels depending on the size of the self-organizing pool of workers.) Conditional on having formed, the self-organizing teams also benefit from several advantages in performance.
Karim R. Lakhani, Hila Lifshitz-Assaf, and Michael L. Tushman. 2013. “
Open Innovation and Organizational Boundaries: Task Decomposition, Knowledge Distribution and the Locus of Innovation.” In Handbook of Economic Organization: Integrating Economic and Organizational Theory,
edited by Anna Grandori, Pp. 355-382. Edward Elgar Publishing, Inc.
Publisher's VersionAbstractThis 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.
Open_Innovation_and_Organizational_Boundaries.pdf Andrew King and Karim R. Lakhani. 2013. “
Using Open Innovation to Identify the Best Ideas.” MIT Sloan Management Review 55 (1).
Publisher's VersionAbstractAs innovation becomes more democratic, many of the best ideas for new products and services no longer originate in well-financed corporate and government laboratories. Instead, they come from almost anywhere and anyone.1 How can companies tap into this distributed knowledge and these diverse skills? Increasingly, organizations are considering using an open-innovation process, but many are finding that making open innovation work can be more complicated than it looks. PepsiCo, the food and beverage giant, for example, created controversy in 2011 when an open-sourced entry into its Super Bowl ad contest that was posted online featured Doritos tortilla chips being used in place of sacramental wafers during Holy Communion. Similarly, Kraft Foods Australia ran into challenges when it launched a new Vegemite-based cheese snack in conjunction with a public naming contest. The name Kraft initially chose from the submissions, iSnack 2.0, encountered widespread ridicule, and Kraft abandoned it. (The company instead asked consumers to choose among six other names. The company ultimately picked the most popular choice among those six, Vegemite Cheesybite.) Reports of such problems have fed uncertainty among managers about how and when to open their innovation processes. Managers tell us that they need a means of categorizing different types of open innovation and a list of key success factors and common problems for each type. Over the last decade, we have worked to create such a guide by studying and researching the emergence of open-innovation systems in numerous sectors of the economy, by working closely with many organizations that have launched open-innovation programs and by running our own experiments.2 This research has allowed us to gain a unique perspective on the opportunities and problems of implementing open-innovation programs. (See “About the Research.”) In every organization and industry, executives were faced with the same decisions. Specifically, they had to determine (1) whether to open the idea-generation process; (2) whether to open the idea-selection process; or (3) whether to open both. These choices led to a number of managerial challenges, and the practices the companies implemented were a major factor in whether the innovation efforts succeeded or failed.
Kevin J. Boudreau and Karim R. Lakhani. 2013. “
Using the Crowd as an Innovation Partner.” Harvard Business Review 91 (4), Pp. 61-69.
Publisher's VersionAbstractFrom Apple to Merck to Wikipedia, more and more organizations are turning to crowds for help in solving their most vexing innovation and research questions, but managers remain understandably cautious. It seems risky and even unnatural to push problems out to vast groups of strangers distributed around the world, particularly for companies built on a history of internal innovation. How can intellectual property be protected? How can a crowdsourced solution be integrated into corporate operations? What about the costs? These concerns are all reasonable, the authors write, but excluding crowdsourcing from the corporate innovation tool kit means losing an opportunity. After a decade of study, they have identified when crowds tend to outperform internal organizations (or not). They outline four ways to tap into crowd-powered problem solving — contests, collaborative communities, complementors, and labor markets — and offer a system for picking the best one in a given situation. Contests, for example, are suited to highly challenging technical, analytical, and scientific problems; design problems; and creative or aesthetic projects. They are akin to running a series of independent experiments that generate multiple solutions—and if those solutions cluster at some extreme, a company can gain insight into where a problem’s “technical frontier” lies. (Internal R&D may generate far less information.)