Employees' training experience in a metaverse environment? Feedback analysis using structural topic modeling

被引:6
|
作者
Saeed, Abubakr [1 ]
Ali, Ashiq [2 ]
Ashfaq, Saira [3 ]
机构
[1] COMSATS Univ Islamabad, Dept Management Sci, Pk Rd, Islamabad, Pakistan
[2] Riphah Int Univ, Management Sci, Islamabad Campus, Rawalpindi, Pakistan
[3] York St John Univ, Management Studies Dept, London Campus, York E14 2BA, England
关键词
Metaverse; Employee training experiences; Reviews; structural topical modeling; TEXT ANALYSIS;
D O I
10.1016/j.techfore.2024.123636
中图分类号
F [经济];
学科分类号
02 ;
摘要
The metaverse has been heralded as the next frontier for fueling strategic business opportunities. A recent surge in business investments in digital technologies-based training applications is witnessed. Metaverse is a technology in training and development landscape that intends to materialize a highly immersive experience by combining the virtual and the real world. Organizations are moving towards a metaverse environment to enhance the interactivity and flexibility of training while maintaining a high quality of their educational content and training plans. However, the existing scholarly work on metaverse tends to be more focused on employees' recruitment and retention functions of human resource, while the training and development function, particularly, the employees' training experience of the metaverse, is largely overlooked. Understanding employees' experiences is critical for businesses to achieve the desired training outcomes. Our study aims to fill this research gap by adopting a novel structural topic model text analysis method to analyze 889 employees' reviews about various training applications in metaverse environment. Specifically, we explored the employees' reviews of leading training platforms STRIVR, Spatial Computing, Mursion, Program ACE, Rewo, Gather, and ARKit. Our initial results reveal 9 topics, of which 5 relate to positive aspects and 4 are potential concerns. In particular, realtime collaboration, enhanced practicality, alignment with technology training, real-time feedback analytics, and customizable learning environments are positive, whereas accessibility and inclusivity, ethical considerations, privacy and security concerns, and cultural resistance are negative aspects. This study highlights the promising potential of the metaverse in improving the training and development functions within human resource management. By leveraging the novel efficiencies that the metaverse confers, firms can use these advancements to gain a competitive advantage.
引用
收藏
页数:15
相关论文
共 50 条
  • [1] Customers' metaverse service encounter perceptions: sentiment analysis and topic modeling
    Sam, S. Jerrin Issac
    Jasim, K. Mohamed
    Babu, Manivannan
    JOURNAL OF HOSPITALITY MARKETING & MANAGEMENT, 2025, 34 (01) : 92 - 114
  • [2] Exploring Hype in Metaverse: Topic Modeling Analysis of Korean Twitter User Data
    Sun, Seungjong
    Kim, Jang-Hyun
    Jung, Hae-Sun
    Kim, Minwoo
    Zhao, Xiangying
    Kamphuis, Pim
    SYSTEMS, 2023, 11 (03):
  • [3] Incorporating structural topic modeling into short text analysis
    Wang, Po-Ya Angela
    Hsieh, Shu-Kai
    CONCENTRIC-STUDIES IN LINGUISTICS, 2023, 49 (01) : 96 - 138
  • [4] Content analysis of newspaper coverage of wolf recolonization in France using structural topic modeling
    Chandelier, Marie
    Steuckardt, Agnes
    Mathevet, Raphael
    Diwersy, Sascha
    Gimenez, Olivier
    BIOLOGICAL CONSERVATION, 2018, 220 : 254 - 261
  • [5] Analyzing Customer Experience in Hotel Services Using Topic Modeling
    Van-Ho Nguyen
    Thanh Ho
    JOURNAL OF INFORMATION PROCESSING SYSTEMS, 2021, 17 (03): : 586 - 598
  • [6] Sarcasmometer using Sentiment Analysis and Topic Modeling
    Bhan, Namrata
    D'silva, Mitchell
    2017 IEEE INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATION AND CONTROL (ICAC3), 2017,
  • [7] Fifty years of information management research: A conceptual structure analysis using structural topic modeling
    Sharma, Anuj
    Rana, Nripendra P.
    Nunkoo, Robin
    INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT, 2021, 58
  • [8] Uncovering employee insights: integrative analysis using structural topic modeling and support vector machines
    Ding, Kai
    Li, Ruihong
    Li, Zeyu
    Hu, Shangui
    JOURNAL OF BIG DATA, 2025, 12 (01)
  • [9] Determinants of Guest Experience in Airbnb: A Topic Modeling Approach Using LDA
    Sutherland, Ian
    Kiatkawsin, Kiattipoom
    SUSTAINABILITY, 2020, 12 (08)
  • [10] Optimal Policies on Education Training for Employees Using Maintenance Modeling Aspects
    Yamashita, Shigeshi
    Kawakami, Sho
    Khoa, Truong Dinh Anh
    Ito, Kodo
    INTERNATIONAL JOURNAL OF RELIABILITY QUALITY AND SAFETY ENGINEERING, 2023, 30 (02)