Now showing 1 - 10 of 18
  • Publication
    How to Empower the Workforce – Analyzing Internal Crowd Work as a Neo-Socio-Technical System
    ( 2019-01-08)
    Durward, David
    ;
    Simmert, Benedikt
    ;
    ; ;
    In this paper, we analyze internal crowd work as Neo-STS from an employee’s perspective. Based on qualitative interviews, we describe in our model how employees perceive empowerment through participation in internal crowd work. As our main contribution, we detail and extend existing research regarding internal crowd work, Neo-STS as well as empowerment by identifying structural antecedents that affect psychological empowerment of internal crowd workers.
    Type:
    Journal:
  • Publication
    Leveraging the Potentials of Dedicated Collaborative Interactive Learning: Conceptual Foundations to Overcome Uncertainty by Human-Machine Collaboration
    ( 2018)
    Calma, Adrian
    ;
    Oeste-Reiß, Sarah
    ;
    Sick, Bernhard
    ;
    When a learning system learns from data that was previously assigned to categories, we say that the learning system learns in a supervised way. By “supervised”, we mean that a higher entity, for example a human, has arranged the data into categories. Fully categorizing the data is cost intensive and time consuming. Moreover, the categories (labels) provided by humans might be subject to uncertainty, as humans are prone to error. This is where dedicate collaborative interactive learning (D-CIL) comes together: The learning system can decide from which data it learns, copes with uncertainty regarding the categories, and does not require a fully labeled dataset. Against this background, we create the foundations of two central challenges in this early development stage of D-CIL: task complexity and uncertainty. We present an approach to “crowdsourcing traffic sign labels with self-assessment” that will support leveraging the potentials of D-CIL.
  • Publication
    Managing Complex Work Systems via Crowdworking Platforms: How Deutsche Bank Explores AI Trends and the Future of Banking with Jovoto
    Crowdsourcing has evolved into a powerful new instrument for companies. In the last years, crowdworking platforms that manage work systems as intermediaries between crowdsourcers and crowd workers have increasingly been used. Nevertheless, they currently often manage rather simple work systems. Although they have the potential for managing more complex ones, there is still little knowledge how this can be done and what measures are necessary to do so. To explore this question in more detail, we investigate three seminal projects that Deutsche Bank completed with the crowdworking platform Jovoto and that aimed at exploring AI trends and developing concepts for the future of banking. We derive measures necessary for the successful management of complex work systems and provide a model as guidance for both companies and crowdworking platform operators. With our findings, we extend current knowledge in the realm of IS, organizational theory, and platform ecosystems.
    Type:
    Journal:
  • Publication
    Managing Complex Work Systems via Crowdworking Platforms: How Intel and Hyve Explore Future Technological Innovations
    Crowdsourcing has the potential to change the way how companies and other organizations are working currently. Numerous companies are already exploiting this new form of work organization and are utilising the “wisdom of crowds”. Crowdworking platforms as intermediaries that manage the work system including customer companies and crowd workers play an important role in this context. Nevertheless, they currently mostly manage rather simple work systems that process rather plain work. In this summary for the HICSS 2018 Doctoral Consortium, we depict our current work in progress that aims at investigating how such platforms could also manage more complex work systems – a question that is crucial for the future success of this business model. Using the case of Intel and the crowdworking platform Hyve, we investigate one successful approach to tackle this challenge, elaborate on our method used as well as the theoretical background and communicate our first, preliminary findings.
    Type:
    Journal:
  • Publication
    Managing Complex Work Systems via Crowdworking Platforms: The Case of Hamburger Hochbahn and Phantominds
    In the last decade, crowdsourcing has emerged as a new form of work organization. Crowdworking platforms as intermediaries between crowdsourcing companies and crowd workers have gained importance in this process. Currently, many of these platforms manage rather simple work systems. Using the case of the German Hamburger Hochbahn AG and the innovation platform Phantominds, this paper investigates measures necessary for crowdworking platforms to be able to manage also more complex work systems. To derive such measures, we analyze the work system of Hamburger Hochbahn and Phantominds, explore the interplay between the crowd and the platform provider and subsequently provide recommendations for companies that would like to use crowdworking platforms for the processing of work and for platform operators. With this paper, we extend current knowledge in the realms of IS, organizational theory, and platform ecosystems.
    Type:
    Journal:
  • Publication
    The Rise of Crowd Aggregators - How Individual Workers Restructure Their Own Crowd
    Crowd work has emerged as a new form of digital gainful employment whose nature is still a black box. In this paper, we focus on the crowd workers – a perspective that has been largely neglected by research. We report results from crowd worker interviews on two different platforms. Our findings illustrate that crowd aggregators as new players restructure the nature of crowd work sustainably with different effects on the behavior as well as the existing relationships of crowd workers. We contribute to prior research by developing a theoretical framework based on value chain and work aggregation theories which are applicable in this new form of digital labor. For practice, our results provide initial insights that need to be taken into account as part of the ongoing discussion on fair and decent conditions in crowd work.
  • Publication
    Work Organization in Online Platform Ecosystems
    Crowdsourcing as a new paradigm how to proceed (paid and unpaid) work has gained momentum in the last years. Numerous companies and other organizations use “the wisdom of crowds” (Surowiecki 2004) for their goals. In the context of the paid part of crowdsourcing that is processed via online platforms one can name “crowdworking platforms” (Mrass et al. 2017c), the World Bank recently predicted in a study a global increase in market volume from 2.1 billion USD in 2013 and 4.8 billion USD in 2016 up to 25 billion USD in 2020 (see Kuek et al. 2015, p. 20-25). However, these and other data about such platforms and their surrounding “ecosystems” heavily rely on estimations using only some platforms and trying to project their data to a greater scale. To the best of knowledge of the author of these lines for the paper-a-thon format, there is no data available so far that covers a larger (e.g. a country) definable region (and let alone the whole world) and that relies not only on estimations, but also on “real” data from crowdworking platforms representative for the “total population” of platforms from that region. This view was also confirmed by the answer to a respective request made by the author of this paper to the central official statistical authority in Germany (Statistisches Bundesamt/see: www.destatis.de) for that region. Nevertheless, such data would be beneficial for several stakeholders: Economy, since many companies currently wonder if they could and should consider crowdworking platforms for the processing of work (Zogaj 2016). Politics, since a lot of questions regarding minimum wage requirements and the status of crowdworkers arose (see e.g. Benner 2014). And not least science, since research about such platforms with the exception of some US-American crowdworking platforms is scarce and would benefit from more data as a basis for further explorations.
    Type:
    Journal:
  • Publication
    Towards a Future Reallocation of Work between Humans and Machines – Taxonomy of Tasks and Interaction Types in the Context of Machine Learning
    ( 2017)
    Traumer, Fabian
    ;
    Oeste-Reiß, Sarah
    ;
    In today’s race for competitive advantages, more and more companies implement innovations in artificial intelligence and machine learning (ML). Although these machines take over tasks that have been executed by humans, they will not make human workforce obsolete. To leverage the potentials of ML, collaboration between humans and machines is necessary. Before collaboration processes can be developed, a classification of tasks in the field of ML is needed. Therefore, we present a taxonomy for the classification of tasks due to their complexity and the type of interaction. To derive insights about typical tasks and task-complexity, we conducted a literature review as well as a focus group workshop. We identified three levels of task-complexity and three types of interactions. Connecting them reveals three generic types of tasks. We provide prescriptive knowledge inherent in the task/interaction-taxonomy.
    Type:
    Journal: