Exploring the Acceptance of Technological Innovation Among Employees in the Mining Industry: A Study on Generative Artificial Intelligence - Wecka Imam Yudhistyra; Chalita Srinuan (IEEE Access, 2024)
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.
Keywords: Big Data and Business Intelligence; Organizational and Employee Performance