Enrique Estellés-Arolas

Bio: Enrique Estellés-Arolas is an academic researcher from University of Valencia. The author has contributed to research in topics: Crowdsourcing & Collective intelligence. The author has an hindex of 4, co-authored 7 publications receiving 1514 citations. Previous affiliations of Enrique Estellés-Arolas include Polytechnic University of Valencia.

Towards an integrated crowdsourcing definition

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.

Crowdsourcing Fundamentals: Definition and Typology

Abstract: Crowdsourcing is a problem-solving and task realization model that is being increasingly used. Thanks to the possibility of harnessing the collective intelligence from the Internet; thanks to the crowdsourcing initiatives people can, for example, find a solution to a complex chemical problem, get images tagged, or get a logo designed. Due to its success and usefulness, more and more researchers have focused their interest on this concept. This fact has shown that the concept of crowdsourcing has no clear boundaries, and although over time the concept has been better explained, some authors describe it differently, propose different types of crowdsourcing initiatives, or even use contradictory crowdsourcing examples. In this paper, an integrated definition and typology, developed in 2012, are analyzed to check whether they are still valid today or whether need a reformulation.

Clasificación de iniciativas de crowdsourcing basada en tareas

Abstract: Crowdsourcing initiatives by organizations working in areas like music, design or cataloguing are becoming more frequent. Nonetheless, the absence of a consistent theoretical background creates problems, such as the existence of diverse crowdsourcing classifications that overlap and interweave, or the lack of a common definition. This paper analyses different typologies considering the nature of the tasks to be performed by the crowd as the main criterion and proposes a new integrative typology.

Using crowdsourcing for a safer society: When the crowd rules:

Abstract: Neighbours sharing information about robberies in their district through social networking platforms, citizens and volunteers posting about the irregularities of political elections on the Internet...

Using Mechanical Turk to Study Clinical Populations

Abstract: Although participants with psychiatric symptoms, specific risk factors, or rare demographic characteristics can be difficult to identify and recruit for participation in research, participants with...

Theory of value co-creation: a systematic literature review

Abstract: Purpose – The purpose of this paper is to summarize and classify extant research and to better understand the past, present, and future state of the theory of value co-creation. Its main objectives are: to identify the different theoretical perspectives and research streams that characterize and define the co-creation literature, and to highlight the connections between them; to look for emerging trends and gaps in the literature by comparing the most recent papers with those representing the field's core. Design/methodology/approach – The paper relies on bibliometric data: co-citation techniques were employed to select, analyze, and interpret citation patterns within the co-creation literature. Findings – The paper identified two main clusters, as well as specific research streams and common themes, representing scholarly journals’ publications on co-creation over the past years. These research streams and themes apply three different theoretical perspectives: service science, innovation and technology m...

Evaluation on crowdsourcing research: Current status and future direction

Abstract: Crowdsourcing is one of the emerging Web 2.0 based phenomenon and has attracted great attention from both practitioners and scholars over the years. It can facilitate the connectivity and collaboration of people, organizations, and societies. We believe that Information Systems scholars are in a unique position to make significant contributions to this emerging research area and consider it as a new research frontier. However, so far, few studies have elaborated what have been achieved and what should be done. This paper seeks to present a critical examination of the substrate of crowdsourcing research by surveying the landscape of existing studies, including theoretical foundations, research methods, and research foci, and identifies several important research directions for IS scholars from three perspectives--the participant, organization, and system--and which warrant further study. This research contributes to the IS literature and provides insights for researchers, designers, policy-makers, and managers to better understand various issues in crowdsourcing systems and projects.

AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images

Abstract: The lack of publicly available ground-truth data has been identified as the major challenge for transferring recent developments in deep learning to the biomedical imaging domain. Though crowdsourcing has enabled annotation of large scale databases for real world images, its application for biomedical purposes requires a deeper understanding and hence, more precise definition of the actual annotation task. The fact that expert tasks are being outsourced to non-expert users may lead to noisy annotations introducing disagreement between users. Despite being a valuable resource for learning annotation models from crowdsourcing, conventional machine-learning methods may have difficulties dealing with noisy annotations during training. In this manuscript, we present a new concept for learning from crowds that handle data aggregation directly as part of the learning process of the convolutional neural network (CNN) via additional crowdsourcing layer (AggNet). Besides, we present an experimental study on learning from crowds designed to answer the following questions. 1) Can deep CNN be trained with data collected from crowdsourcing? 2) How to adapt the CNN to train on multiple types of annotation datasets (ground truth and crowd-based)? 3) How does the choice of annotation and aggregation affect the accuracy? Our experimental setup involved Annot8, a self-implemented web-platform based on Crowdflower API realizing image annotation tasks for a publicly available biomedical image database. Our results give valuable insights into the functionality of deep CNN learning from crowd annotations and prove the necessity of data aggregation integration.

Modelling with stakeholders - Next generation

Abstract: This paper updates and builds on 'Modelling with Stakeholders' Voinov and Bousquet, 2010 which demonstrated the importance of, and demand for, stakeholder participation in resource and environmental modelling. This position paper returns to the concepts of that publication and reviews the progress made since 2010. A new development is the wide introduction and acceptance of social media and web applications, which dramatically changes the context and scale of stakeholder interactions and participation. Technology advances make it easier to incorporate information in interactive formats via visualization and games to augment participatory experiences. Citizens as stakeholders are increasingly demanding to be engaged in planning decisions that affect them and their communities, at scales from local to global. How people interact with and access models and data is rapidly evolving. In turn, this requires changes in how models are built, packaged, and disseminated: citizens are less in awe of experts and external authorities, and they are increasingly aware of their own capabilities to provide inputs to planning processes, including models. The continued acceleration of environmental degradation and natural resource depletion accompanies these societal changes, even as there is a growing acceptance of the need to transition to alternative, possibly very different, life styles. Substantive transitions cannot occur without significant changes in human behaviour and perceptions. The important and diverse roles that models can play in guiding human behaviour, and in disseminating and increasing societal knowledge, are a feature of stakeholder processes today. Display Omitted Participatory modelling has become mainstream in resource and environmental management.We review recent contributions to participatory environmental modelling to identify the tools, methods and processes applied.Global internet connectivity, social media and crowdsourcing create opportunities for participatory modelling.We suggest structured mechanisms to examine and account for human biases and beliefs in participatory modelling.Advanced visualization tools, gaming, and virtual environments improve communication with stakeholders.