Search Results for « Crowdsourcing »

Abstract Data collection in consumer research has progressively moved away from traditional samples (e.g., university undergraduates) and toward Internet samples. In the last complete volume of the Journal of Consumer Research (June 2015–April 2016), 43% of behavioral studies were conducted on the crowdsourcing website Amazon Mechanical Turk (MTurk). The option to crowdsource empirical investigations has great efficiency benefits for both individual researchers and the field, but it also poses new challenges and questions for how research should be designed, conducted, analyzed, and evaluated. We assess the evidence on the reliability of crowdsourced populations and the conditions under which crowdsourcing is a valid strategy for data collection. Based on this evidence, we propose specific guidelines for researchers to conduct high-quality research via crowdsourcing. We hope this tutorial will strengthen the community’s scrutiny on data collection practices and move the field toward better and more valid crowdsourcing of consumer research.

‘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.

Online contract labor portals (i.e., crowdsourcing) have recently emerged as attractive alternatives to university participant pools for the purposes of collecting survey data for behavioral research. However, prior research has not provided a thorough examination of crowdsourced data for organizational psychology research. We found that, as compared with a traditional university participant pool, crowdsourcing respondents were older, were more ethnically diverse, and had more work experience. Additionally, the reliability of the data from the crowdsourcing sample was as good as or better than the corresponding university sample. Moreover, measurement invariance generally held across these groups. We conclude that the use of these labor portals is an efficient and appropriate alternative to a university participant pool, despite small differences in personality and socially desirable responding across the samples. The risks and advantages of crowdsourcing are outlined, and an overview of practical and ethical guidelines is provided.

The word Crowdsourcing –a compound contraction of Crowd and Outsourcing, was used by Howe in order to define outsourcing to the crowd. Beyond cost, benefits for the company can be substantial. It can externalize the risk of failure and it only pays for products or services that meet its expectations. The Crowdsourcing phenomenon covers heterogeneous situations and it has inspired a number of authors. However, we are still lacking a general and synthetic view of this concept. The aim of our work is to characterize Crowdsourcing in its various aspects. First we define of Crowdsourcing, and provide examples that illustrate the diversity of Crowdsourcing practices and we present similarities and differences between Crowdsourcing and established theories (Open Innovation, User Innovation and Open Source Software). Then, we propose and illustrate a typology of Crowdsourcing practices based on two criteria: the integrative or selective nature of the process and the type of tasks that are crowd sourced (simple, complex and creative tasks). In either case, the client firm seeks to mobilize external competencies. Relying upon the crowd can be an adequate method, because of its unique characteristics that are fostered by the Internet. Finally, we present some potential benefits and pitfalls of Crowdsourcing.JEL Codes: O32, L17, L24, L29, M19

MOTIVATION: Bioinformatics is faced with a variety of problems that require human involvement. Tasks like genome annotation, image analysis, knowledge-base population and protein structure determination all benefit from human input. In some cases, people are needed in vast quantities, whereas in others, we need just a few with rare abilities. Crowdsourcing encompasses an emerging collection of approaches for harnessing such distributed human intelligence. Recently, the bioinformatics community has begun to apply crowdsourcing in a variety of contexts, yet few resources are available that describe how these human-powered systems work and how to use them effectively in scientific domains. RESULTS: Here, we provide a framework for understanding and applying several different types of crowdsourcing. The framework considers two broad classes: systems for solving large-volume 'microtasks' and systems for solving high-difficulty 'megatasks'. Within these classes, we discuss system types, including volunteer labor, games with a purpose, microtask markets and open innovation contests. We illustrate each system type with successful examples in bioinformatics and conclude with a guide for matching problems to crowdsourcing solutions that highlights the positives and negatives of different approaches.

We argue that under certain circumstances crowdsourcing transforms distant search into local search, improving the efficiency and effectiveness of problem solving. Under such circumstances a firm may choose to crowdsource problem solving rather than solve the problem internally or contract it to a designated supplier. These circumstances depend on the characteristics of the problem, the knowledge required for the solution, the crowd, and the solutions to be evaluated.

This study investigates how different gamification implementations can increase crowdsourcees' motivation and participation in crowdsourcing (CS). To this end, we review empirical literature that has investigated the use of gamification in crowdsourcing settings. Overall, the results of the review indicate that gamification has been an effective approach for increasing crowdsourcing participation. When comparing crowdcreating, -solving, -processing and-rating CS approaches, the results show differences in the use of gamification across CS types. Crowdsourcing initiatives that provide more monotonous tasks most commonly used mere points and other simpler gamification implementations, whereas CS initiatives that seek for diverse and creative contributions have employed gamification in more manifold ways employing a richer set of mechanics. These findings provide insights for designers of gamified systems and further research on the topics of gamification and crowdsourcing.

Crowdsourcing is a newly developed term which refers to the process of outsourcing of activities by a firm to an online community or crowd in the form of an ‘open call’. Any member of the crowd can then complete an assigned task and be paid for their efforts. Although this form of labour organisation was pioneered in the computing sector, businesses have started to use ‘crowdsourcing’ for a diverse range of tasks that they find can be better completed by members of a crowd rather than by their own employees. This paper examines how firms are utilising crowdsourcing for the completion of marketing-related tasks, concentrating on the three broad areas of product development, advertising and promotion, and marketing research. It is found that some firms are using crowdsourcing to locate large numbers of individuals willing to complete largely menial repetitive tasks for limited financial compensation. Other firms utilise crowdsourcing to solicit solutions to particular tasks from a crowd of diverse and/or expert opinions. Conclusions are drawn regarding the advantages and the limitations of crowdsourcing and the potential for the future use of crowdsourcing in additional marketing-related applications. Keywords: Crowdsourcing, Outsourcing, Wikinomics

Even though considerable attention has been given to the polarity of words (positive and negative) and the creation of large polarity lexicons, research in emotion analysis has had to rely on limited and small emotion lexicons. In this paper, we show how the combined strength and wisdom of the crowds can be used to generate a large, high‐quality, word–emotion and word–polarity association lexicon quickly and inexpensively. We enumerate the challenges in emotion annotation in a crowdsourcing scenario and propose solutions to address them. Most notably, in addition to questions about emotions associated with terms, we show how the inclusion of a word choice question can discourage malicious data entry, help to identify instances where the annotator may not be familiar with the target term (allowing us to reject such annotations), and help to obtain annotations at sense level (rather than at word level). We conducted experiments on how to formulate the emotion‐annotation questions, and show that asking if a term is associated with an emotion leads to markedly higher interannotator agreement than that obtained by asking if a term evokes an emotion.

Abstract As academic and practitioner studies on crowdsourcing have been building up since 2006, the subject itself has progressively gained in importance within the broad field of management. No systematic review on the topic has so far appeared in management journals, however; moreover, the field suffers from ambiguity in the topic's definition, which in turn has led to its largely unstructured evolution. The authors therefore investigate the existing body of knowledge on crowdsourcing systematically through a penetrating review in which the strengths and weakness of this literature stream are presented clearly and then future avenues of research are set out. The review is based on 121 scientific articles published between January 2006 and January 2015. The review recognizes that crowdsourcing is ingrained in two mainstream disciplines within the broader subject matter of innovation and management: (1) open innovation; and (2) co‐creation. The review, in addition, also touches on several issues covered in other theoretical streams: (3) information systems management; (4) organizational theory and design; (5) marketing; and (6) strategy. The authors adopt a process perspective, applying the ‘Input–Process–Output’ framework to interpret research on crowdsourcing within the broad lines of: (1) Input (Problem/Task); (2) Process (session management; problem management; knowledge management; technology); and (3) Outcome (solution/completed task; seekers’ benefits; solvers’ benefits). This framework provides a detailed description of how the topic has evolved over time, and suggestions concerning the future direction of research are proposed in the form of research questions that are valuable for both academics and managers.