{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T09:22:48Z","timestamp":1778664168488,"version":"3.51.4"},"reference-count":35,"publisher":"Association for Computing Machinery (ACM)","issue":"CSCW","license":[{"start":{"date-parts":[[2017,12,6]],"date-time":"2017-12-06T00:00:00Z","timestamp":1512518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Wilma Bradley Fellowship in Computer Science & Engineering"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Hum.-Comput. Interact."],"published-print":{"date-parts":[[2017,12,6]]},"abstract":"<jats:p>Understanding whether and how motivation affects participation in online experiments is critical because who contributes and how they contribute can affect the validity of findings. Analyzing data from 7,674 participants across three different studies on the volunteer-based online experiment platform LabintheWild, we identified five motivation types for participating: boredom, comparison, fun, science, and self-learning. We found that these motivation types affect study selection, attention, and dropouts. Participants who were highly motivated by boredom paid less attention and were more likely to dropout than those who were motivated by the possibility of contributing to science. We additionally show that motivation can impact study results and suggest how researchers can take participants' motivation into account when designing and analyzing data from volunteer-based online experiments.<\/jats:p>","DOI":"10.1145\/3134691","type":"journal-article","created":{"date-parts":[[2017,12,6]],"date-time":"2017-12-06T21:23:15Z","timestamp":1512595395000},"page":"1-15","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":42,"title":["Types of Motivation Affect Study Selection, Attention, and Dropouts in Online Experiments"],"prefix":"10.1145","volume":"1","author":[{"given":"Eunice","family":"Jun","sequence":"first","affiliation":[{"name":"University of Washington, Seattle, WA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gary","family":"Hsieh","sequence":"additional","affiliation":[{"name":"University of Washington, Seattle, WA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Katharina","family":"Reinecke","sequence":"additional","affiliation":[{"name":"University of Washington, Seattle, WA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2017,12,6]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2207676.2208699"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/pan\/mpr057"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1146\/annurev.psych.55.090902.141601"},{"key":"e_1_2_1_4_1","volume-title":"Quasi-experimental designs. 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