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Crowdsourcing: Why the Power of the Crowd Is Driving the Future of Business

18 Aug 2008-
TL;DR: The idea of crowdsourcing was first identified by journalist Jeff Howe in a June 2006 Wired article as mentioned in this paper, which describes the process by which the power of the many can be leveraged to accomplish feats that were once the province of the specialized few.
Abstract: The amount of knowledge and talent dispersed among the human race has always outstripped our capacity to harness it Crowdsourcing corrects thatbut in doing so, it also unleashes the forces of creative destruction From CrowdsourcingFirst identified by journalist Jeff Howe in a June 2006 Wired article, crowdsourcing describes the process by which the power of the many can be leveraged to accomplish feats that were once the province of the specialized few Howe reveals that the crowd is more than wiseits talented, creative, and stunningly productive Crowdsourcing activates the transformative power of todays technology, liberating the latent potential within us all Its a perfect meritocracy, where age, gender, race, education, and job history no longer matter; the quality of work is all that counts; and every field is open to people of every imaginable background If you can perform the service, design the product, or solve the problem, youve got the jobBut crowdsourcing has also triggered a dramatic shift in the way work is organized, talent is employed, research is conducted, and products are made and marketed As the crowd comes to supplant traditional forms of labor, pain and disruption are inevitable Jeff Howe delves into both the positive and negative consequences of this intriguing phenomenon Through extensive reporting from the front lines of this revolution, he employs a brilliant array of stories to look at the economic, cultural, business, and political implications of crowdsourcing How were a bunch of part-time dabblers in finance able to help an investment company consistently beat the market? Why does Procter & Gamble repeatedly call on enthusiastic amateurs to solve scientific and technical challenges? How can companies as diverse as iStockphoto and Threadless employ just a handful of people, yet generate millions of dollars in revenue every year? The answers lie within these pages The blueprint for crowdsourcing originated from a handful of computer programmers who showed that a community of like-minded peers could create better products than a corporate behemoth like Microsoft Jeff Howe tracks the amazing migration of this new model of production, showing the potential of the Internet to create human networks that can divvy up and make quick work of otherwise overwhelming tasks One of the most intriguing ideas of Crowdsourcing is that the knowledge to solve intractable problemsa cure for cancer, for instancemay already exist within the warp and weave of this infinite and, as yet, largely untapped resource But first, Howe proposes, we need to banish preconceived notions of how such problems are solved The very concept of crowdsourcing stands at odds with centuries of practice Yet, for the digital natives soon to enter the workforce, the technologies and principles behind crowdsourcing are perfectly intuitive This generation collaborates, shares, remixes, and creates with a fluency and ease the rest of us can hardly understand Crowdsourcing, just now starting to emerge, will in a short time simply be the way things are done
Citations
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Journal ArticleDOI
TL;DR: In this article, the implications and trends that underpin open innovation are discussed in terms of strategic, organizational, behavioral, knowledge, legal and business perspectives and its economic implications, and a special issue aims to advance the R&D, innovation and technology management perspective by building on past and present studies in the field and providing future directions.
Abstract: There is currently a broad awareness of open innovation and its relevance to corporate R&D. The implications and trends that underpin open innovation are actively discussed in terms of strategic, organizational, behavioral, knowledge, legal and business perspectives and its economic implications. This special issue aims to advance the R&D, innovation, and technology management perspective by building on past and present studies in the field and providing future directions. Recent research, including the papers in this special issue, demonstrates an increasing range of situations where the concept is regarded as applicable. Most research to date has followed the outside-in process of open innovation, while the inside-out process remains less explored. A third coupled process of open innovation is also attracting significant research attention. These different processes show why it is necessary to have a full understanding of how and where open innovation can add value in knowledge-intensive processes. There may be a need for a creative interpretation and adaptation of the value propositions, or business models, in each situation. In other words, there are important implications for new and emerging methods of R&D management.

1,787 citations

Journal ArticleDOI
TL;DR: In this article, existing definitions of crowdsourcing are analysed to extract common elements and to establish the basic characteristics of any crowdsourcing initiative.
Abstract: 'Crowdsourcing' is a relatively recent concept that encompasses many practices. This diversity leads to the blurring of the limits of crowdsourcing that may be identified virtually with any type of internet-based collaborative activity, such as co-creation or user innovation. Varying definitions of crowdsourcing exist, and therefore some authors present certain specific examples of crowdsourcing as paradigmatic, while others present the same examples as the opposite. In this article, existing definitions of crowdsourcing are analysed to extract common elements and to establish the basic characteristics of any crowdsourcing initiative. Based on these existing definitions, an exhaustive and consistent definition for crowdsourcing is presented and contrasted in 11 cases.

1,616 citations

Journal ArticleDOI
TL;DR: In this article, the authors compare two forms of crowdfunding: entrepreneurs solicit individuals either to pre-order the product or to advance a fixed amount of money in exchange for a share of future profits (or equity).

1,573 citations

Journal ArticleDOI
TL;DR: In this paper, the authors compare two forms of crowdfunding: entrepreneurs solicit individuals either to pre-order the product or to advance a fixed amount of money in exchange for a share of future profits (or equity).
Abstract: With crowdfunding, an entrepreneur raises external financing from a large audience (the "crowd"), in which each individual provides a very small amount, instead of soliciting a small group of sophisticated investors. This article compares two forms of crowdfunding: entrepreneurs solicit individuals either to pre-order the product or to advance a fixed amount of money in exchange for a share of future profits (or equity). In either case, we assume that "crowdfunders" enjoy "community benefits" that increase their utility. Using a unified model, we show that the entrepreneur prefers pre-ordering if the initial capital requirement is relatively small compared with market size and prefers profit sharing otherwise. Our conclusions have implications for managerial decisions in the early development stage of firms, when the entrepreneur needs to build a community of individuals with whom he or she must interact. We also offer extensions on the impact of quality uncertainty and information asymmetry.

1,400 citations

Journal ArticleDOI
TL;DR: A probabilistic approach for supervised learning when the authors have multiple annotators providing (possibly noisy) labels but no absolute gold standard, and experimental results indicate that the proposed method is superior to the commonly used majority voting baseline.
Abstract: For many supervised learning tasks it may be infeasible (or very expensive) to obtain objective and reliable labels. Instead, we can collect subjective (possibly noisy) labels from multiple experts or annotators. In practice, there is a substantial amount of disagreement among the annotators, and hence it is of great practical interest to address conventional supervised learning problems in this scenario. In this paper we describe a probabilistic approach for supervised learning when we have multiple annotators providing (possibly noisy) labels but no absolute gold standard. The proposed algorithm evaluates the different experts and also gives an estimate of the actual hidden labels. Experimental results indicate that the proposed method is superior to the commonly used majority voting baseline.

1,344 citations


Additional excerpts

  • ...With the advent of crowdsourcing (Howe, 2008) services like Amazon’s Mechanical Turk,3 Games with a Purpose,4 and reCAPTCHA5 it is quite inexpensive to acquire labels from a large number of annotators (possibly thousands) in a short time (Sheng et al., 2008; Snow et al., 2008; Sorokin and Forsyth,…...

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