TY - JOUR
T1 - From frequency to fatigue
T2 - Exploring the influence of videoconference use on videoconference fatigue in Singapore
AU - Li, Benjamin J.
AU - Lee, Edmund W.J.
AU - Goh, Zhang Hao
AU - Tandoc, Edson
N1 - Funding Information:
This project was supported by the Center for Information Integrity and the Internet (IN-cube), Nanyang Technological University, Singapore , and by the Ministry of Education Singapore through Academic Research Fund Tier 1 Grants ( RG34/21 and RG150/18 ).
Publisher Copyright:
© 2022 The Authors
PY - 2022/8
Y1 - 2022/8
N2 - The use of videoconferencing platforms has increased drastically as a result of the COVID-19 pandemic. As a result of work-from-home orders, many employees found themselves attending meetings through virtual communication technologies instead of usual face-to-face discussions. As employees spend more time on videoconferencing, there have been increasing concerns of users affected by an occurrence we define as videoconference fatigue (VF). In this study, we explore the link between frequency of videoconferencing and VF. We further explore videoconference users' satisfaction with their internet connection as a moderator of this relationship. We study these in the context of the Technology Acceptance Model (TAM), which provides a framework for us to understand the factors leading to VF. A survey was conducted in Singapore with 1145 respondents who use videoconference apps. Results from structural equation modeling supported a model where perceived ease of use of videoconference apps led to perceived usefulness of these apps, which led to an increased frequency of use. There was a significant relationship between frequency of use and feelings of videoconference fatigue, with this relationship moderated by users’ perceived satisfaction with their internet connection. When usage frequency is low, having a reliable internet connection helps mitigate the impact of use on VF. However, high levels of usage can override the mitigating impact of internet satisfaction. We discuss the implications of these findings, which lend understanding into potential factors that can result in VF.
AB - The use of videoconferencing platforms has increased drastically as a result of the COVID-19 pandemic. As a result of work-from-home orders, many employees found themselves attending meetings through virtual communication technologies instead of usual face-to-face discussions. As employees spend more time on videoconferencing, there have been increasing concerns of users affected by an occurrence we define as videoconference fatigue (VF). In this study, we explore the link between frequency of videoconferencing and VF. We further explore videoconference users' satisfaction with their internet connection as a moderator of this relationship. We study these in the context of the Technology Acceptance Model (TAM), which provides a framework for us to understand the factors leading to VF. A survey was conducted in Singapore with 1145 respondents who use videoconference apps. Results from structural equation modeling supported a model where perceived ease of use of videoconference apps led to perceived usefulness of these apps, which led to an increased frequency of use. There was a significant relationship between frequency of use and feelings of videoconference fatigue, with this relationship moderated by users’ perceived satisfaction with their internet connection. When usage frequency is low, having a reliable internet connection helps mitigate the impact of use on VF. However, high levels of usage can override the mitigating impact of internet satisfaction. We discuss the implications of these findings, which lend understanding into potential factors that can result in VF.
KW - Internet satisfaction
KW - Technology acceptance model
KW - Videoconference fatigue
KW - Videoconference use
KW - Wellbeing
UR - http://www.scopus.com/inward/record.url?scp=85132874867&partnerID=8YFLogxK
U2 - 10.1016/j.chbr.2022.100214
DO - 10.1016/j.chbr.2022.100214
M3 - Article
AN - SCOPUS:85132874867
SN - 2451-9588
VL - 7
JO - Computers in Human Behavior Reports
JF - Computers in Human Behavior Reports
M1 - 100214
ER -