22 & 23 May 2025: Join the mini-conference on Open and FAIR in Natural and Engineering Sciences. Register to attend.

Data and code: Customizable and Evolving Avatars to Improve Worker Engagement and Foster a Sense of Community in Online Crowd Work

DOI:10.4121/d6c4bb4c-fa4c-441a-888e-2ac8b8d9468c.v1
The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/d6c4bb4c-fa4c-441a-888e-2ac8b8d9468c

Datacite citation style

de Groot, Esra; Gadiraju, Ujwal (2025): Data and code: Customizable and Evolving Avatars to Improve Worker Engagement and Foster a Sense of Community in Online Crowd Work. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/d6c4bb4c-fa4c-441a-888e-2ac8b8d9468c.v1
Other citation styles (APA, Harvard, MLA, Vancouver, Chicago, IEEE) available at Datacite

Dataset

This folder contains the data and code related to the paper below:


de Groot, E. C. S., & Gadiraju, U. (2024, May). " Are we all in the same boat?" Customizable and Evolving Avatars to Improve Worker Engagement and Foster a Sense of Community in Online Crowd Work. In Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems (pp. 1-26).


Paper abstract:

Human intelligence continues to be essential in building ground-truth data, training sets, and for evaluating a plethora of systems. The democratized and distributed nature of online crowd work — an attractive and accessible feature that has led to the proliferation of the paradigm — has also meant that crowd workers may not always feel connected to their remote peers. Despite the prevalence of collaborative crowdsourcing practices, workers on many microtask crowdsourcing platforms work on tasks individually and are seldom directly exposed to other crowd workers. In this context, improving worker engagement on microtask crowdsourcing platforms is an unsolved challenge. At the same time, fostering a sense of community among workers can improve the sustainability and working conditions in crowd work. This work aims to increase worker engagement in conversational microtask crowdsourcing by leveraging evolving avatars that workers can customize as they progress through monotonous task batches. We also aim to improve group identification in individual tasks by creating a community space where workers can share their avatars and feelings on task completion. To this end, we carried out a preregistered between-subjects controlled study (N = 680) spanning five experimental conditions and two task types. We found that evolving and customizable worker avatars can increase worker retention. The prospect of sharing worker avatars and task-related feelings in a community space did not consistently affect group identification. Our exploratory analysis indicated that workers who identify themselves as crowd workers experienced greater intrinsic motivation, subjective engagement, and perceived workload. Furthermore, we discuss how task differences shape the relative effectiveness of our interventions. Our findings have important theoretical and practical implications for designing conversational crowdsourcing tasks and in shaping new directions for research to improve crowd worker experiences.

History

  • 2025-03-10 first online, published, posted

Publisher

4TU.ResearchData

Format

.xlsx/ .csv/ .zip/ .Rmd / .ipynb

Funding

  • This research is (partially) funded by the Convergence, the alliance between Erasmus Medical Centre Rotterdam, Erasmus University Rotterdam, and Delft University of Technology.

Organizations

TU Delft, Faculty of Electrical Engineering, Mathematics and Computer Science, Department of Software Technology, Web Information Systems

DATA

Files (12)