Crowdsourced data in public administration research: A review and look to the future

• 成果类型: Review
• 署名作者: Stritch, Justin M.; Pedersen, Mogens Jin; Pezo, Ignacio
• 署名单位: Arizona State University; Arizona State University-Downtown Phoenix; University of Copenhagen
• 刊物名称: PUBLIC ADMINISTRATION REVIEW
• ISSN/ISSBN: 0033-3352
• DOI: 10.1111/puar.13823
• 发表日期: 2025
• 页码: 581-593
• 关键词: MECHANICAL TURK

摘要

Crowdsourcing platforms such as MTurk and Prolific have emerged as data sources for researchers in the social sciences. This article delves into the past, present, and future use of crowdsourced data in public administration scholarship. Through a review of published articles in top public administration journals (years 2013-2022), we uncover a general growth in the use of crowdsourced data over time. Additionally, we document how researchers have leveraged crowdsourced data to study a diverse range of themes and topics, with particular emphasis on survey experimental approaches and the examination of citizen attitudes and responses. Moreover, drawing on insights from a survey among quantitative public administration researchers, we discuss why the use of crowdsourced data is unlikely to diminish in the foreseeable future-despite ongoing debates regarding data quality and validity. We provide a set of guiding questions for researchers to consider when using crowdsourced data in public administration studies. Evidence for practice Data collected via crowdsourcing platforms can be appropriate and meaningfully used to study important themes and topics within public administration and management. The use of crowdsourced data in public administration scholarship has shown an upward trajectory over time. Researchers have leveraged crowdsourced data-particularly emphasizing survey experimental approaches and focusing on citizen attitudes and responses-to examine important research questions pertaining to policy support, performance assessments and evaluations, trust and legitimacy, public service delivery mechanisms and sector bias, as well as motivation. Given pertinent concerns surrounding data quality and validity, researchers employing crowdsourced data should design their studies carefully, taking steps to address these issues to the extent possible.