Exploring the Acceptance of Technological Innovation Among Employees in the Mining Industry: A Study on Generative Artificial Intelligence

Wecka Imam Yudhistyra · Chalita Srinuan

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Technology Adoption and User Behaviour AI and HR Technologies Impact of AI and Big Data on Business and Society

Abstract

Generative Artificial Intelligence (GenAI) technology innovation holds promise for revolutionizing the mining industry. However, the sector’s traditionally conservative stance toward innovation has led to limited research. Thus, this manuscript aims to address this gap by exploring factors influencing the acceptance of GenAI technology innovation among mining industry employees from a developing country perspective. Based on literature reviews seven factors were identified to influence employees’ acceptance of GenAI technological innovation. These factors were examined in a data sample of 286 mining industry employees collected via an office-intercept survey and analyzed with Partial Least Squares Structural Equation Modeling (PLS-SEM) using the SmartPLS software. Through thorough data analysis, models have been constructed that exhibit robustness, evidenced by their strong validity, reliability, and excellent fit with the data. The findings reveal that attitude, perceived usefulness, perceived ease of use, compatibility, company size, industrial competitiveness, and top management support significantly influence the acceptance of GenAI technology innovation. Notably, attitude was identified as the most influential factor, significantly shaping the likelihood of adoption. Conversely, company size had a comparatively minor impact on acceptance, and the regulatory framework showed no significant effect on the adoption of GenAI technology innovation. These results have important theoretical and managerial implications. Theoretically, they provide a nuanced understanding of the key factors driving technology acceptance in the mining industry, challenging the traditional emphasis on regulatory frameworks. Managerially, they underscore the importance of focusing on factors when developing strategies to foster technological adoption.

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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)1994Marketing Research: An Applied Orientation 2003Diffusion of innovations 2019Assessing measurement model quality in PLS-SEM using confirmatory composite analysis 1993User acceptance of information technology: system characteristics, user perceptions and behavioral impacts

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