PUB - Publikationen an der Universität Bielefeld

Abstract Crowdsourcing describes a type of participative online activity in which an organization proposes a task to a group of individuals via a flexible open call (Estellés-Arolas & González-Ladrón-de-Guevara, 2012). This study focuses on a literature review of crowdsourcing platforms especially for paid work on so-called “crowdworking platforms”. The main contributions are the overview of empirical studies on the subject from a personnel econom-ics and psychological perspective and the devel-opment of a comprehensive Input-Process-Output Model (IPO-Model) from the workers’ perspective. In the remainder of this abstract, we will discuss the focus, theoretical background and design of our systematic review. Research on crowd work is heterogeneous in na-ture and driven by multiple disciplines. Not sur-prisingly, researchers have already conducted literature reviews (e.g. Chittilappilly, Chen, & Amer-Yahia, 2016; Ghezzi, Gabelloni, Martini, & Natalicchio, 2017; Kittur et al., 2013; Zhao & Zhu, 2014). However, these focus broadly of the crowdsourcing concept, thereby not differentiat-ing whether it is a digital gainful work or an un-paid voluntary participation. Such reviews usual-ly study what crowdsourcing is, how it is differ-ent from similar or related concepts, and how crowdsourcing works (conceptualization focus) (Zhao & Zhu, 2014). They also discuss how crowdsourcing is applied in different situations and for different purposes. This review contributes to the previous literature by evaluating existing research systematically and describing empirical connections between con-structs by grouping similar research into clusters. In contrast to past research which approaches crowdsourcing holistically, we focus specifically on crowd work, i.e. crowd sourcing in which contributors are paid and enter a particular em-ployment relationship with the platform. We are particularly interested in outcomes of this rela-tionship for the individual crowd worker. Since crowdworking has important similarities to other types of work both typical (e.g. permanent and temporary employments) and atypical (e.g. teleworking, freelancing, self-employment), ex-tant research in personnel economics and person-nel psychology can be used to shed light on the factors that might influence crowdworking initia-tives. We reviewed 91 empirical articles, systematically codified these studies and developed an IPO-Model from a personnel economics and psycho-logical perspective. IPO-Models are widely used in sciences for describing processes in system analysis and mechanisms of action in research. Studies were identified by applying a number of search terms: crowd work*, crowdwork*, crowd sourc*, crowdsource*, platform economy, gig economy or crowd employment. The most im-portant databases for both psychology and eco-nomics were searched, namely PsycINFO, Econ-Lit and Business Source Complete. Due to a high number of hits, the search was narrowed down to empirical studies. Additionally, we applied a backward and forward search strategy on the references of key articles. This search resulted in 1173 primary studies overall. We selected rele-vant primary studies by applying three selection criteria. The studies had to (1) report research on the construct of crowdworking, (2) show an em-phasis on personnel economic and psychological research questions, (3) and collect empirical data. As a result, 91 studies remained and were sys-tematically codified by publication data; infor-mation about sample, crowdworking platform, research design, methodology and findings. An iterative bottom-up approach then aggregated these codified variables into clusters based on similarity and content-related proximity. The clusters are divided into the three stages of the IPO-model, namely input, process and output. The input variables were grouped into seven clus-ters: monetary incentives; nonmonetary incen-tives; task design; market-related variables; workers’ qualification/profile; workers’ traits/characteristics; individual working history on the platform. The input variables were mod-eled in primary studies to explain the variations of specific process- or output variables. The output variables were grouped into six clus-ters: job satisfaction, worker commitment to-wards the platform, participation in crowd work, qualitative performance, quantitative perfor-mance and employability of the crowd worker. Involved process variables which potentially moderate or mediate the relationship between input and output variables were grouped into six clusters: workers’ intrinsic and extrinsic motiva-tion; worker’s affect; worker’s perceived compe-tence; invested effort for task completion; work-ers’ trust towards the platform and workers’ per-ceived fairness of the processes on crowdwork-ing platforms. Further analyses of the literature expand the IPO-Model by information about statistically signifi-cant and non-significant relations. Hence, our review shows how often a research question has been addressed and which statistical effects evolved in the studies. Overall, our review provides a roadmap for fu-ture research on the topic of crowd work as digi-tal gainful work. We identify and quantify the state of the art in current research of personal economics and psychology on the topic of crowd work. Our review has important implications on how to enhance factors that are critical to worker and platforms alike, such as attraction, motiva-tion and commitment of self-employed workers on crowdworking platforms. References Chittilappilly, A. I., Chen, L., & Amer-Yahia, S. 2016. A Survey of General-Purpose Crowdsourcing Techniques. IEEE Transac-tions on Knowledge and Data Engineering, 28(9): 2246–2266. Estellés-Arolas, E., & González-Ladrón-de-Guevara, F. 2012. Towards an integrated crowdsourcing definition. Journal of Infor-mation Science, 38(2): 189–200. Ghezzi, A., et al. 2017. Crowdsourcing: A review and suggestions for future research. Interna-tional Journal of Management Reviews. Kittur, A., et al. 2013. The future of crowd work, Proceedings of the 2013 conference on Computer supported cooperative work: 1301–1318. Zhao, Y., & Zhu, Q. 2014. Evaluation on crowdsourcing research: Current status and future direction. Information Systems Fron-tiers, 16(3): 417–434.