Year: 2024
Type: article
Study design: Observational Study
Abstract: Metaverse technology (MT) signifies a computer-generated sphere where operators can interconnect in a virtual setting. The adoption of MT has the possibility to transform business operation methods, business-to-customer interaction, and firm-worker collaboration approaches. On the other hand, some problems should be tackled and overcome to guarantee the success of adopting the MT. The research seeks to investigate MT adoption in manufacturing firms, emphasising estimating possible hindrances to present effective adoption plans. The data for this study was gathered from 311 participants in manufacturing companies in Palestine. This study uses machine learning tools, namely artificial neural network (ANN) modelling, combined with structural equation modelling (SEM) to assess the theoretical framework. The SEM findings showed that a company's technological inadequacies are the most critical obstacle to MT adoption in manufacturing firms. The ANN findings underlined the company's technological inadequacies as the most vital input, poor regulation and governance, incorporation barriers, low perceived usefulness among consumers, weak collaborative actions, low dedication from stakeholders, conservative company culture, and low transmission over the networks. As MT is prospective to offer companies competitive benefits, better efficiency, enhanced consumer involvement and stimulated innovation, it is imperative to deliberate and foster resolutions to adoption risks in the businesses. Firms can achieve successful adoption of the intriguing and rapidly everchanging technological environment by solving those risks/barriers. The research gives new understandings to MT inventors and managers for effective MT adoption in manufacturing firms and novel conceptual insights for practitioners intending to adopt this technology in their manufacturing firms.
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4407185160