服务业用户生成内容的映射研究——文献计量分析

Elżbieta Wąsowicz-Zaborek
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引用次数: 0

摘要

Web 2.0时代和Web发展的后续阶段给企业带来了新的挑战,但也给企业带来了与市场参与者建立和维护关系、与客户直接接触并了解他们的需求、情感和意见的新机遇。内容创作和共享技术的进步创造了从任何可以访问互联网的人那里收集信息的机会。用户生成内容(UGC)信息越来越多地支持各种商业、管理或营销活动的决策和分析。这类信息也越来越多地被用作科学研究中的数据来源。本研究旨在评估UGC在科学研究中的相关性,以及互联网用户创建的内容可以被研究服务部门中存在的现象的研究人员使用的范围和方式。为了实现这一目标,我们对2012年至2022年间发表在Scopus数据库索引期刊上的文章进行了文献计量学文献综述(出版物定量分析、研究合作者识别、合著者分析、共被引分析和共词分析)。分析采用描述性统计和文本及内容分析。在2020年至2022年期间,出版物数量显著增加。在各种服务部门中,研究人员最常选择的数据集是旅游业客户在网上发布的评论,主要是那些使用住宿服务的客户,也包括餐馆。TripAdvisor被认为是最常用的数据源。在他们的分析中,作者使用了定性和定量方法,以及两者的结合。可以观察到,更复杂的机器学习算法已经被用于文本分析。最后,提出了今后的研究建议。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mapping Research on User-Generated Content in the Service Sector — A Bibliometric Analysis
Abstract The Web 2.0 era and the following phases of web development bring new challenges to businesses, but also new opportunities to establish and maintain relationships with market participants, indulge in direct contact with customers and learn about their needs, emotions and opinions. The advancement of content creation and sharing technologies creates an opportunity to collect information from anyone with access to the Internet. User-generated content (UGC) information is increasingly supporting decision-making and analysis for various types of business, management or marketing activities. Such information is also increasingly used as a source of data in scientific research. The present study seeks to evaluate the relevance of UGC in scientific research and the scope and ways in which content created by Internet users can be used by researchers of phenomena existing in the service sector. To achieve this goal, a bibliometric literature review (quantitative analysis of publications, identification of research collaborators, co-author analysis, co-citation analysis and co-word analysis) was conducted covering articles between 2012 and 2022 published in journals indexed in the Scopus database. The analysis used descriptive statistics and text and content analysis. A significant increase was observed in publications between 2020 and 2022. Among the various service branches, the researchers most often chose data sets in the form of comments posted online by customers of tourism industries, mainly those using accommodation services, but also restaurants. TripAdvisor was observed to be the most frequently used data source. In their analysis, the authors used both qualitative and quantitative methods, as well as a combination of them. It is observed that more sophisticated machine learning algorithms have been implemented for text analysis. Finally, the paper also presents future research recommendations.
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