Publication Type

Journal Article

Version

publishedVersion

Publication Date

1-2025

Abstract

The metaverse is the next-generation Internet (Web3) that facilitates social connections and collaborations in a virtual world environment. Given the potential of the metaverse to provide more satisfying and effective means of remote collaborations, exploring the possibility of leveraging the metaverse for these endeavors is warranted. Therefore, an important question to address is whether greater engagement occurs when tasks are completed collaboratively versus individually in the metaverse. We address this question by drawing on flow and transportation theories to hypothesize the effect of carrying out a creative task in the metaverse collaboratively versus alone on one's cognitive absorption, a contextually relevant proxy for the flow experience. In the context of the metaverse, cognitive absorption refers to the heightened enjoyment experienced when one is immersed and “transported” into the metaverse while maintaining a sense of curiosity and control as well as perceiving a distorted sense of time. We conducted a laboratory experiment to test our research hypotheses. The results indicate that collaborations in the metaverse enhance cognitive absorption. Cognitive absorption, in turn, increases outcome satisfaction and intention to use the metaverse. The findings provide theoretical contributions by enhancing the nomological network of cognitive absorption as well as explaining how computer-mediated collaborations can facilitate the virtual transportation of users into the metaverse. The findings also offer insights and guidance for enhancing cognitive absorption and outcome satisfaction in the metaverse as well as the intention to use the metaverse.

Keywords

Cognitive absorptions, Collaboration, Curiosity, Enjoyment, Flow, Focused concentration, Metaverses, Satisfaction, Temporal dissociation, Transportation theory, Use intentions

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering; Information Systems and Management

Publication

Decision Support Systems

Volume

188

ISSN

0167-9236

Identifier

10.1016/j.dss.2024.114346

Publisher

Elsevier

Additional URL

https://doi.org/10.1016/j.dss.2024.114346

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