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Article
Publication date: 28 March 2024

Yajun Guo, Huifang Ma, Jiahua Zhou, Yanchen Chen and Yiming Yuan

This article aims to understand users' information needs in the metaverse communities and to analyze the similarities and differences between their information needs and those of…

Abstract

Purpose

This article aims to understand users' information needs in the metaverse communities and to analyze the similarities and differences between their information needs and those of users in Internet communities.

Design/methodology/approach

This study conducted semi-structured interviews with users in the metaverse communities to gather raw data. Grounded theory research methods were employed to code and analyze the collected interview data, resulting in the extraction of 40 initial concepts, 15 subcategories and 5 main categories. Based on Maslow’s hierarchy of needs theory, this paper constructs the hierarchical model of users' information needs in the metaverse communities. It compares the differences between users' information needs in the metaverse and Internet fields.

Findings

The user’s information needs in the metaverse communities are divided into two types: deficiency needs and growth needs. Deficiency needs have two levels. The first level is the demand for basic information resources. The second level is the users demand for information assistance. Growth needs have three levels. The first level is the need for information interactions. The second level is the need for community rules. The ownership information in the community rules can provide proof of user status, assets and so on. The third level is the need for users to contribute and share their own created information content.

Originality/value

This article presents the latest research data from in-depth interviews with users in the metaverse communities. It aims to help builders and managers of metaverse communities understand users' information needs and improve the design of virtual communities.

Details

Library Hi Tech, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0737-8831

Keywords

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