| Id | 2250 | |
| Author | Zhang X.; Zhang W.; Zhao Y.C.; Zhu Q. | |
| Title | Imbalanced volunteer engagement in cultural heritage crowdsourcing: a task-related exploration based on causal inference | |
| Reference | Zhang X.; Zhang W.; Zhao Y.C.; Zhu Q. Imbalanced volunteer engagement in cultural heritage crowdsourcing: a task-related exploration based on causal inference,Information Processing and Management 59 5 |
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| Link to article | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85134780556&doi=10.1016%2fj.ipm.2022.103027&partnerID=40&md5=828691353554b296a4a9e384f2773c20 |
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| Abstract | As the crowdsourcing approach is increasingly being used for digitizing cultural heritage artifacts, there is a rising need for volunteer engagement in such collaborative digital humanities projects. This study focuses on the less explored topic of imbalanced volunteer engagement (IVE); it refers to the fact that most volunteers tend to focus only on a small portion of tasks, making it challenging to sustain cultural heritage crowdsourcing (CHC) projects. Using a public dataset containing 145,168,535 items captured from the Australian Newspaper Digitisation Project, we utilized a machine learning-based causal inference approach to investigate the IVE problem by examining the causal relationships between task content characteristics and volunteer engagements. We used the directed acyclic graph (DAG) to represent the structure, such that a causal relationship consisting of 11 nodes and 16 edges was obtained. Specifically, four causes, including task category, word count, number of task lists, and whether the task was illustrated, directly affect IVE. We further discuss these findings from a theoretical perspective and suggest three propositions: a) nudge-like intervention of a task list, b) subjective (perceived) low task complexity, and c) attraction of task presentation, alleviating the IVE problem. This study contributes to the literature on volunteer engagement in the CHC context and sheds new light on the design and implementation of collaborative digital humanities projects. © 2022 |
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| Keywords | Directed graphs; Causal inferences; Causal relationships; Cultural heritage crowdsourcing; Cultural heritages; Digital humanities; Digitisation; Imbalanced volunteer engagement; Machine-learning; Public dataset; Task content characteristic; Crowdsourcing |
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