Users’ attention behaviors and features in internet forum
Abstract: Attention resource
is scarce. Organizing community activities in online forums faces the challenge
of attracting users’ limited attention. Understanding how users of online
forums allocate, maintain, and change their attentional focus and what features
of online forms influence their attention behaviors is critical for effective
information design. This paper seeks understanding of users’ attention
behaviors and features when they participate in discussions in online forums.
Design/methodology/approach: A conceptual model was established to
explore the indicator system of attention’s measurement. The related attention
data were collected from Alexa Access Statistics Tool and Katie community. Then
this paper computed the correlation coefficient and regression relationship
between the indicators of visual attention and cognitive attention. Thereafter
this paper analyzed and discussed users’ attention behaviors and features in
Internet forum.
Findings: Relevant bivariate correlation analysis and regression analysis
discovers that Internet forum's attention is mainly as visual attention in
users’ early involvement. Attention resources can be transformed. In a deep
participation, users’ cognitive attention is more significant. Meanwhile
cognitive attention behaviors’ further development will lead to the phenomenon
that cognitive attention input is prone to increase faster in the early
duration. That means in-depth discussion and interaction are more likely to
appear in the early stages of participation.
Research limitations/implications: There are some limitations about this
study. The indicators are not comprehensive enough because factors affecting
the distribution of attention resources in Internet forums are complex. We
didn’t distinguish different types of Internet forums when we collected the
relevant data. Future research will focus more on how to obtain comprehensive
attention data.
Originality/value: T his paper shows a new perspective that we can find
users’ attention behaviors and features using the attention data from its
mapping object, which can help operators of portals and Internet communities to
attract users’ limited attention.
Author: Yong-Zhong Sha, Li Lu
Journal Code: jptindustrigg150083
