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3In dit boekje over crowdsourcing worden een aantal relevante aspecten van crowdsourcing behandeld. Allereerst beschrijven we een aantal historische voorbeelden om duidelijk te maken dat crowdsourcing niet ontstaan is als gevolg van de opkomst van internet maar als fenomeen al bestond voor het internettijdperk. Door internet is het echter zonder meer eenvoudiger geworden crowdsourcing te organiseren en een veel grotere groepen deelnemers te betrekken. In de sectie 'Wisdom of the Crowds' gaan we in op de onderliggende principes van crowdsourcing. Crowdsourcing wordt vaak in één adem genoemd met de 'Wisdom of the Crowds', als onderliggend mechanisme hoe en waarom crowdsourcing werkt. We zullen echter concluderen dat de 'Wisdom of the Crowds' slechts één van de drie onderliggende principes van crowdsourcing is. Vervolgens gaan we in op de verschillende verschijningsvormen van crowdsourcing. Na een reflectie op bestaande voorstellen om tot een categorisering te komen van deze verschijningsvormen, presenteren we zeven categorieën op basis van het te onderscheiden doel. Bij de keuze om crowdsourcing in te zetten zal naar de kosten, risico's en baten ervan gekeken moeten worden. In de sectie 'Kosten en baten van crowdsourcing' bekijken we dit aspect voornamelijk vanuit het perspectief van de initiërende organisatie. Maar de kosten en baten voor de deelnemers zullen ook kort beschreven worden om te begrijpen wat hen drijft om aan een crowdsourcingproject mee te doen. In de laatste sectie beantwoorden we de vraag hoe crowdsourcing zo effectief en efficiënt mogelijk is in te zetten door naar een aantal implementatiemodellen te kijken en algemene adviezen te inventariseren. We sluiten af met een reflectie op de beschreven bevindingen.
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Artikel op SportNext over hoe crowdsourcing ingezet kan worden om een sportevenement, in dit geval een wielerkoers, innovatiever en aantrekkelijker te maken.
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The term crowdsourcing was introduced by Jeff Howe (2006). It is the act of a company or organisation to take a function once performed by employees and outsourcing it to an undefined, and usually large, network of people in the form of an open call. As communication tools to organize work have become widely available, and a well-educated global work force has come online, crowdsourcing has become an increasingly important mechanism to organize work. We discuss a categorisation of crowdsourcing, its costs and benefits and several examples. The use of crowdsourcing begins with the question which strategic goal an organisation wants to achieve, and whether the benefits outweigh the costs. We give some recommendations for adopting crowdsourcing. This usually requires a certain amount of restructuring of existing workflows and a willingness to become more open which may or may not be a welcome side effect.
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Op welke wijze kan crowdsourcing worden ingezet om te komen tot een landelijk platform waar innovatieve ideeën voor onderwijsvernieuwing in het HBO worden verzameld en gewaardeerd.
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Crowdsourcing is niet nieuw. Er zijn tal van historische voorbeelden waarbij ‘het volk’ opgeroepen wordt vrijwillig bij te dragen aan een probleem of vraag. Met de komst van het internet is het echter veel eenvoudiger geworden een grote groep te enthousiasmeren, het proces efficiënt te begeleiden en de deelnemers blijvend te stimuleren. Bedrijven luisteren al langere tijd naar klanten, gebruiken hun input en hebben focusgroepen om behoeften te achterhalen. Maar nu kan ‘de klant’ op een grotere en directere manier worden benaderd dan ooit (Bonabeau, 2009).
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Blog voor SportNext over een nieuw besturingsconcept dat in verschillende landen geprobeerd wordt, het besturen van een voetbalclub middels de crowdsourcing principes.
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Companies use crowdsourcing to solve specific problems or to search for innovation. By using open innovation platforms, where community members propose ideas, companies can better serve customer needs. So far, it remains unclear which factors influence idea implementation in crowd sourcing context. With the research idea that we present here, we aim to get a better understanding of the success and failure of ideas by examining relationships between characteristics of ideators, characteristics of ideas and the likelihood of implementation. In order to test the methodological approach that we propose in this paper in which we investigate for business relevant innovativeness as well as sentiment based on text analytics, data including unstructured text was mined from Dell IdeaStorm using webcrawling and scraping techniques. Some relevant hypotheses that we define in this paper were confirmed on the Dell IdeaStorm dataset but in order to generalize our findings we want to apply to the Leg o dataset in our current work in progress. Possible implications of our novel research idea can be used to fill theoretical gaps in marketing literature, help companies to better structure their search for innovation and for ideators to better understand factors contributing to successful idea generation.
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A common challenge with processing naturalistic driving data is that humans may need to categorize great volumes of recorded visual information. By means of the online platform CrowdFlower, we investigated the potential of crowdsourcing to categorize driving scene features (i.e., presence of other road users, straight road segments, etc.) at greater scale than a single person or a small team of researchers would be capable of. In total, 200 workers from 46 different countries participated in 1.5 days. Validity and reliability were examined, both with and without embedding researcher generated control questions via the CrowdFlower mechanism known as Gold Test Questions (GTQs).
By employing GTQs, we found significantly more valid (accurate) and reliable (consistent) identification of driving scene items from external workers. Specifically, at a small scale CrowdFlower Job of 48 three-second video segments, an accuracy (i.e., relative to the ratings of a confederate researcher) of 91% on items was found with GTQs compared to 78% without. A difference in bias was found, where without GTQs, external workers returned more false positives than with GTQs. At a larger scale CrowdFlower Job making exclusive use of GTQs, 12,862 three-second video segments were released for annotation. Infeasible (and self-defeating) to check the accuracy of each at this scale, a random subset of 1012 categorizations was validated and returned similar levels of accuracy (95%).
In the small scale Job, where full video segments were repeated in triplicate, the percentage of unanimous agreement on the items was found significantly more consistent when using GTQs (90%) than without them (65%). Additionally, in the larger scale Job (where a single second of a video segment was overlapped by ratings of three sequentially neighboring segments), a mean unanimity of 94% was obtained with validated-as-correct ratings and 91% with non-validated ratings. Because the video segments overlapped in full for the small scale Job, and in part for the larger scale Job, it should be noted that such reliability reported here may not be directly comparable. Nonetheless, such results are both indicative of high levels of obtained rating reliability.
Overall, our results provide compelling evidence for CrowdFlower, via use of GTQs, being able to yield more accurate and consistent crowdsourced categorizations of naturalistic driving scene contents than when used without such a control mechanism. Such annotations in such short periods of time present a potentially powerful resource in driving research and driving automation development.
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Player Modeling is a research field that studies player characteristics by analyzing in-game behavior. We aim to develop independent models, which are transferable and useful beyond a game’s context. We shall demonstrate the feasibility of this approach by applying player models to crowdsourcing to predict workers’ task completion effectiveness. Specifically, we model a user’s Need for Cognition based on in-game behavior, and based on that try to assign appropriate tasks to workers.
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The question of how to make a city or government better by exploiting information and communication infrastructures, referred to as smart city, entails an emerging field of research. Large quantities of data are generated from these infrastructures and infusing these data into the physical infrastructure of a city or government may lead to better services to citizens. Collecting and processing of such data, however, may result in privacy and security issues that should be faced appropriately to create a sustainable approach for smart cities and governments. In this chapter, we focus on data collection through crowdsourcing with smart devices and identify the corresponding security and privacy issues in the context of enabling smart cities and governments. We categorize these issues in four classes. For each class, we identify a number of threats as well as solution directions for these threats.
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