DC Field | Value | Language |
---|---|---|
dc.contributor.author | Anagnostopoulos, Theodoros | - |
dc.contributor.author | Hosio, Simo | - |
dc.contributor.author | Ferreira, Denzil | - |
dc.contributor.author | Van Berkel, Niels | - |
dc.contributor.author | Luo, Chu | - |
dc.contributor.author | Kostakos, Vassilis | - |
dc.contributor.author | Goncalves, Jorge | - |
dc.date.accessioned | 2024-07-09T12:36:18Z | - |
dc.date.available | 2024-07-09T12:36:18Z | - |
dc.date.issued | 2016-05-07 | - |
dc.identifier | scopus-84991436016 | - |
dc.identifier.isbn | 978-1-4503-3362-7 | - |
dc.identifier.other | 84991436016 | - |
dc.identifier.uri | https://uniwacris.uniwa.gr/handle/3000/2697 | - |
dc.description.abstract | Researchers who analyse smartphone usage logs often make the assumption that users who lock and unlock their phone for brief periods of time (e.g., less than a minute) are continuing the same "session" of interaction. However, this assumption is not empirically validated, and in fact different studies apply different arbitrary thresholds in their analysis. To validate this assumption, we conducted a field study where we collected user-labelled activity data through ESM and sensor logging. Our results indicate that for the majority of instances where users return to their smartphone, i.e., unlock their device, they in fact begin a new session as opposed to continuing a previous one. Our findings suggest that the commonly used approach of ignoring brief standby periods is not reliable, but optimisation is possible. We therefore propose various metrics related to usage sessions and evaluate various machine learning approaches to classify gaps in usage. | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | CHI '16: Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems | en_US |
dc.subject | Classification models | en_US |
dc.subject | ESM | en_US |
dc.subject | Human behaviour | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Mobile devices | en_US |
dc.subject | Phone usage | en_US |
dc.subject | Session | en_US |
dc.title | A systematic assessment of smartphone usage gaps | en_US |
dc.type | Conference Paper | en_US |
dc.relation.conference | CHI '16: CHI Conference on Human Factors in Computing Systems, 7-12 May 2016, San Jose California, USA | en_US |
dc.identifier.doi | 10.1145/2858036.2858348 | en_US |
dc.identifier.scopus | 2-s2.0-84991436016 | - |
dcterms.accessRights | 0 | en_US |
dc.relation.dept | Department of Business Administration | en_US |
dc.relation.faculty | School of Administrative, Economics and Social Sciences | en_US |
dc.identifier.spage | 4711 | en_US |
dc.identifier.epage | 4721 | en_US |
dc.collaboration | University of West Attica (UNIWA) | en_US |
dc.journals | Open Access | en_US |
dc.publication | Peer Reviewed | en_US |
dc.country | Greece | en_US |
local.metadatastatus | not verified | en_US |
item.openairetype | Conference Paper | - |
item.grantfulltext | none | - |
item.fulltext | No Fulltext | - |
item.cerifentitytype | Publications | - |
item.openairecristype | http://purl.org/coar/resource_type/c_18cf | - |
item.languageiso639-1 | en | - |
crisitem.author.dept | Department of Business Administration | - |
crisitem.author.faculty | School of Administrative, Economics and Social Sciences | - |
crisitem.author.orcid | 0000-0002-5587-2848 | - |
crisitem.author.parentorg | School of Administrative, Economics and Social Sciences | - |
Appears in Collections: | Book Chapter / Κεφάλαιο Βιβλίου |
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