How Does the Crowdsourcing Experience Impact Participants\u27 Engagement? An Empirical Illustration

A largely neglected aspect in crowdsourcing research is the “Crowdsourcing Experience” itself, which every crowdsourcee is necessarily exposed to throughout the IT-mediated interaction process, potentially stimulating engagement towards the crowdsourcer. Hence, the crowdsourcees’ engagement process is conceptualized and illustrated with empirical findings from a pilot case. It exemplifies that crowdsourcing has the potential to generate high levels of attitudinal and behavioral engagement, depending on prior experiences and perceived cognitions and emotions. Related stimuli characteristics are identified, which serve as a first indication of the foundations of the engagement process. This study offers IS-researchers first insights on the so far under-researched topic of IT-enabled engagement processes between individuals and entities

Blueprinting Crowdfunding - Designing a Crowdfunding Service Configuration Framework

Crowdfunding gained momentum over the last few years. In contrast to traditional forms of funding, the service provision of crowdfunding platforms is performed within service systems. These comprise a complex combination of IT and non-IT services, different stakeholders, and diverging contexts and purposes. The design and operation of such service systems represents a tough challenge. Therefore, we developed a crowdfunding service configuration framework in the form of a morphological box and derived three dominant design patterns by following a design science approach. Therefore, we followed three iterations, which comprise in total twelve expert interviews, three case studies and the analysis of 161 crowdfunding platforms. The configuration framework extends research on crowdfunding and service science by providing insights in how to support the systematic design of crowdfunding service systems, reducing their complexity, and giving a comprehensive overview over their building blocks

I AM A CROWD WORKER – HOW INDIVIDUALS IDENTIFY WITH A NEW FORM OF DIGITAL WORK

Crowd work has emerged as a new form of digital gainful employment that changes the nature of work. However, an increasing number of people perform certain tasks in the crowd and start to identify with this work. In this paper, we outline our research in progress which is concerned with the effects of work characteristics in crowd work that have impact on the individual’s identification. Thus, we developed our research model and conducted an online survey amongst 434 crowd workers to ex-amine their perception of work and illustrate the antecedences of identification. Our expected contribution will increase the understanding of crowd work and extend prior research on self-determination theory (SDT) and work design. For practice, we provide important insights for platform providers to (re-) design work on platform in order to increase identification among their crowd. In addition, our findings can serve as common basis for future discussions on decent crowd work

A Machine Learning Approach for Classifying Textual Data in Crowdsourcing

Crowdsourcing represents an innovative approach that allows companies to engage a diverse network of people over the internet and use their collective creativity, expertise, or workforce for completing tasks that have previously been performed by dedicated employees or contractors. However, the process of reviewing and filtering the large amount of solutions, ideas, or feedback submitted by a crowd is a latent challenge. Identifying valuable inputs and separating them from low quality contributions that cannot be used by the companies is time-consuming and cost-intensive. In this study, we build upon the principles of text mining and machine learning to partially automatize this process. Our results show that it is possible to explain and predict the quality of crowdsourced contributions based on a set of textual features. We use these textual features to train and evaluate a classification algorithm capable of automatically filtering textual contributions in crowdsourcing

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

The Effectiveness of Governance Mechanisms in Crowdfunding

During the last years, crowdfunding gained attention as alternative source of funding for a variety of projects. More and more creative, artistic and entrepreneurial projects search funding through the crowd. However, crowdfunding markets are often considered inefficient and shaped by information asymmetries. Although first project characteristics towards governance mechanisms have been identified, the general use of governance mechanisms in crowdfunding and their impact on funding success have mostly remained uncovered. With that in mind, we present preliminary results on the influence of governance mechanisms on funding success of crowdfunding projects. We assessed 108 projects from 18 platforms in order to measure the use of governance mechanisms and to discover differences between the types of crowdfunding. We find that archetypes of governance mechanisms with influence on the funding success exist and intend to contribute to theory that explains the use of governance mechanisms in crowdfunding

Patterns of Data-Driven Decision-Making: How Decision-Makers Leverage Crowdsourced Data

Crowdsourcing represents a powerful approach for organizations to collect data from large networks of people. While research already made great strides to develop the technological foundations for processing crowdsourced data, little is known about decision-making patterns that emerge when decision-makers have access to such large amounts of data on people’s behavior, opinions, or ideas. In this study, we analyze the characteristics of decision-making in crowdsourcing based on interviews with decision-makers across 10 multinational corporations. For research, we identify four common patterns of decision-making that range from structured and goal-oriented to highly dynamic and data-driven. In this way, we systematize how decision-makers typically source, process, and use crowdsourced data to inform decisions. We also provide an integrated perspective on how different types of decision problems and modes of acquiring information induce such patterns. For practice, we discuss how information systems should be designed to provide adequate support for these patterns

Generative AI in Idea Development: The Role of Numeric and Visual Feedback

Human creativity is a crucial factor in developing innovative ideas. Many ideas are being generated, but only a few receive feedback, as creating feedback is a costly and time-consuming effort in innovation. While feedback promises higher idea quality, previous work requires human experts with domain expertise. Generative AI could provide automated feedback and is expected to transform creative work. This short paper presents an experimental series in which we let humans collaborate with generative AI to develop ideas. Based on dual-coding and media synchronicity theory, we conceptualize numerical and visual feedback to overcome cognitive barriers. We manipulate feedback modalities and timing to personalize the interaction. Our contributions provide evidence on when and why specific co-creative arrangements between humans and generative AI are favorable

Conceptualizing the Agile Work Organization: A systematic literature review, framework and research agenda

The ongoing discussion of the Agile Work Organization (AO) in research and practice permeates a multitude of research areas. However, no clear conceptualization of the AO has been provided. In this paper, we conduct a Systematic Literature Review to investigate what constitutes and defines the AO. The SLR reveals three dimensions in the research field of the AO: Strategic, Functional and Operative Agility. These dimensions define the AO through different unique capabilities by influencing and enhancing the overall goal of the AO in adaptation and flexibility. Building up on the insights from the review, we develop proposition which describe the interrelationship between the dimensions and towards the AO. Furthermore, implications for academia and practice as well as a research agenda are provided in order to trigger and guide further discussions and research surrounding the AO