Personas and Analytics: A Comparative User Study of Efficiency and Effectiveness for a User Identification Task

dc.contributor.authorJoni Salminen
dc.contributor.authorSoon-Gyo Jung
dc.contributor.authorShammur Chowdhury
dc.contributor.authorSercan Şengün
dc.contributor.authorBernard J. Jansen
dc.contributor.organizationfi=markkinointi|en=Marketing|
dc.contributor.organization-code1.2.246.10.2458963.20.50826905346
dc.converis.publication-id50747694
dc.converis.urlhttps://research.utu.fi/converis/portal/Publication/50747694
dc.date.accessioned2022-10-28T13:48:33Z
dc.date.available2022-10-28T13:48:33Z
dc.description.abstractQuantitative persona creation (QPC) has tremendous potential, as HCI researchers and practitioners can leverage user data from online analytics and digital media platforms to better understand their users and customers. However, there is a lack of a systematic overview of the QPC methods and progress made, with no standard methodology or known best practices. To address this gap, we review 49 QPC research articles from 2005 to 2019. Results indicate three stages of QPC research: Emergence, Diversification, and Sophistication. Sharing resources, such as datasets, code, and algorithms, is crucial to achieving the next stage (Maturity). For practitioners, we provide guiding questions for assessing QPC readiness in organizations.C1 - Honolulu, Hawaii, USAC3 - Proceedings of the ACM Conference of Human Factors in Computing Systems (CHI'20)
dc.identifier.isbn978-1-4503-6708-0
dc.identifier.olddbid184455
dc.identifier.oldhandle10024/167549
dc.identifier.urihttps://www.utupub.fi/handle/11111/49884
dc.identifier.urlhttps://dl.acm.org/doi/pdf/10.1145/3313831.3376770
dc.identifier.urnURN:NBN:fi-fe2021042823607
dc.language.isoen
dc.okm.affiliatedauthorSalminen, Joni
dc.okm.discipline113 Computer and information sciencesen_GB
dc.okm.discipline113 Tietojenkäsittely ja informaatiotieteetfi_FI
dc.okm.internationalcopublicationinternational co-publication
dc.okm.internationalityInternational publication
dc.okm.typeA4 Conference Article
dc.publisher.countryUnited Statesen_GB
dc.publisher.countryYhdysvallat (USA)fi_FI
dc.publisher.country-codeUS
dc.relation.conferenceACM SIGCHI annual conference on human factors in computing systems
dc.relation.doi10.1145/3313831.3376770
dc.source.identifierhttps://www.utupub.fi/handle/10024/167549
dc.titlePersonas and Analytics: A Comparative User Study of Efficiency and Effectiveness for a User Identification Task
dc.title.bookProceedings of the 2020 CHI Conference on Human Factors in Computing Systems
dc.year.issued2020

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