Articlegold2025

AI-powered mixed reality acceptance in mining: A PLS-SEM and Bayesian Network modeling

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Technology Adoption and User Behaviour Information Systems Theories and Implementation Technostress in Professional Settings

Abstract

Facilitating digital transformation and sustainable management in the mining industry requires a strategic understanding of how emerging technologies are perceived and adopted by the workforce. Given the sector’s traditionally conservative culture and its resistance to change, there remains a pressing need for empirical investigations that illuminate the pathways toward successful innovation adoption. This study explores the acceptance of AI-powered Mixed Reality (AIPMR) technology among the mining workforce in Indonesia, focusing on its potential to revolutionize human-machine interaction and contribute to smart mining solutions. Drawing upon the Technology Acceptance Model (TAM), an extended conceptual framework was developed to examine the influence of six key factors on employees’ intentions to adopt AIPMR technologies. Data were collected from 304 mining employees and analyzed using Partial Least Squares–Structural Equation Modeling (PLS-SEM), further complemented by Bayesian Network analysis to enhance predictive robustness and uncover probabilistic interdependencies. The empirical results demonstrate that perceived usefulness, perceived ease of use, perceived novelty, top management support, and corporate culture significantly influence employees' attitudes toward adopting AIPMR technology, which subsequently impacts their acceptance of this innovation. The model in this research accounts for 72.6% of the variance in intention to adopt AIPMR technology innovation. This research contributes to the literature by offering a data-driven foundation for developing decision support systems that align with the socio-technical dynamics of the mining industry. It also provides actionable insights for stakeholders seeking to implement technology acceptance strategies that facilitate sustainable digital transformation through the integration of AI-powered Mixed Reality in high-risk industrial environments.

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References

1989Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology 2014A new criterion for assessing discriminant validity in variance-based structural equation modeling 2018When to use and how to report the results of PLS-SEM 2015A primer on partial least squares structural equation modeling (PLS-SEM)2009\nThe use of partial least squares path modeling in international marketing 1994Marketing Research: An Applied Orientation 1993User acceptance of information technology: system characteristics, user perceptions and behavioral impacts 2012Lateral Collinearity and Misleading Results in Variance-Based SEM: An Illustration and Recommendations

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