Characterizing Reading Time on Enterprise Emails
Email is an integral part of people's work and life, enabling them to perform activities such as communicating, searching, managing tasks and storing information. Modern email clients take a step forward and help improve users' productivity by automatically creating reminders, tasks or res...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
03.01.2020
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Subjects | |
Online Access | Get full text |
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Summary: | Email is an integral part of people's work and life, enabling them to perform
activities such as communicating, searching, managing tasks and storing
information. Modern email clients take a step forward and help improve users'
productivity by automatically creating reminders, tasks or responses. The act
of reading is arguably the only activity that is in common in most -- if not
all -- of the interactions that users have with their emails.
In this paper, we characterize how users read their enterprise emails, and
reveal the various contextual factors that impact reading time. Our approach
starts with a reading time analysis based on the reading events from a major
email platform, followed by a user study to provide explanations for some
discoveries. We identify multiple temporal and user contextual factors that are
correlated with reading time. For instance, email reading time is correlated
with user devices: on desktop reading time increases through the morning and
peaks at noon but on mobile it increases through the evening till midnight. The
reading time is also negatively correlated with the screen size.
We have established the connection between user status and reading time:
users spend more time reading emails when they have fewer meetings and busy
hours during the day. In addition, we find that users also reread emails across
devices. Among the cross-device reading events, 76% of reread emails are first
visited on mobile and then on desktop. Overall, our study is the first to
characterize enterprise email reading time on a very large scale. The findings
provide insights to develop better metrics and user models for understanding
and improving email interactions. |
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DOI: | 10.48550/arxiv.2001.00802 |