{"id":215215,"date":"2016-05-01T00:00:00","date_gmt":"2016-05-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/impact-chain-reactions-the-of-order-on-microtask-chains\/"},"modified":"2018-10-16T21:42:19","modified_gmt":"2018-10-17T04:42:19","slug":"impact-chain-reactions-the-of-order-on-microtask-chains","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/impact-chain-reactions-the-of-order-on-microtask-chains\/","title":{"rendered":"Chain Reactions: The Impact of Order on Microtask Chains"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Microtasks are small units of work designed to be completed individually, eventually contributing to a larger goal. Although microtasks can be performed in isolation, in practice people often complete a chain of microtasks within a single session. Through a series of crowd-based studies, we look at how various microtasks can be chained together to improve efficiency and minimize mental demand, focusing on the writing domain. We find that participants completed low-complexity microtasks faster when they were preceded by the same type of microtask, whereas they found high-complexity microtasks less mentally demanding when preceded by microtasks on the same content. Furthermore, participants were faster at starting high-complexity microtasks after completing lower-complexity microtasks, but completion time and quality were not affected. These findings provide insight into how microtasks can be ordered to optimize transitions from one microtask to another.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Microtasks are small units of work designed to be completed individually, eventually contributing to a larger goal. Although microtasks can be performed in isolation, in practice people often complete a chain of microtasks within a single session. Through a series of crowd-based studies, we look at how various microtasks can be chained together to improve [&hellip;]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"footnotes":""},"msr-content-type":[3],"msr-research-highlight":[],"research-area":[13554],"msr-publication-type":[193716],"msr-product-type":[],"msr-focus-area":[],"msr-platform":[],"msr-download-source":[],"msr-locale":[268875],"msr-field-of-study":[],"msr-conference":[],"msr-journal":[],"msr-impact-theme":[],"msr-pillar":[],"class_list":["post-215215","msr-research-item","type-msr-research-item","status-publish","hentry","msr-research-area-human-computer-interaction","msr-locale-en_us"],"msr_publishername":"ACM - Association for Computing Machinery","msr_edition":"CHI 2016","msr_affiliation":"","msr_published_date":"2016-05-01","msr_host":"","msr_duration":"","msr_version":"","msr_speaker":"","msr_other_contributors":"","msr_booktitle":"","msr_pages_string":"","msr_chapter":"","msr_isbn":"","msr_journal":"","msr_volume":"","msr_number":"","msr_editors":"","msr_series":"","msr_issue":"","msr_organization":"","msr_how_published":"","msr_notes":"","msr_highlight_text":"","msr_release_tracker_id":"","msr_original_fields_of_study":"","msr_download_urls":"","msr_external_url":"","msr_secondary_video_url":"","msr_longbiography":"","msr_microsoftintellectualproperty":1,"msr_main_download":"225466","msr_publicationurl":"","msr_doi":"10.1145\/2858036.2858237","msr_publication_uploader":[{"type":"file","title":"chi16-chains.pdf","viewUrl":"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2016\/05\/chi16-chains.pdf","id":225466,"label_id":0},{"type":"doi","title":"10.1145\/2858036.2858237","viewUrl":false,"id":false,"label_id":0}],"msr_related_uploader":"","msr_attachments":[],"msr-author-ordering":[{"type":"text","value":"Carrie J. 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