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 technology adoption.

Mixed Reality (MR) technology is an innovative technology that has the capability to transform and boost productivity in the mining industry. At the same time, the scarcity of guidelines and research in examining the acceptance of MR technology innovation in the mining sector could make the adoption of MR technology innovation unpredictable. Therefore, in order to support the successful adoption of MR technology innovation in the mining sector, this manuscript is designed to better understand the factors influencing its acceptance among workers. Based on the literature review, five factors are hypothesized to influence the acceptance of mining industry workers regarding MR technology innovation. The factors underwent testing on a sample of 253 mining industry workers, which was obtained via an office-intercept survey and processed using SmartPLS software. The model has proven to be highly robust, as indicated by its outstanding scores in validity, reliability, multicollinearity, and goodness of fit. The results from the analysis indicate that attitude, perceived usefulness, and perceived compatibility are significant factors influencing the acceptance of MR technology innovation. While perceived novelty is an important factor affecting attitude, it does not significantly impact the acceptance of MR technology innovation. Moreover, perceived ease of use was not found to be a significant factor in influencing the attitude and intention to adopt MR technology innovation. This research offers both theoretical and managerial implications for its findings.

Energy-efficient consumption has become a strategic priority to mitigate global climate change and enhance national energy security. While social media has reshaped online consumption behavior, the mechanisms through which these digital platforms influence energy-efficient purchasing remain underexplored. This study extends the Theory of Planned Behavior (TPB) by integrating price perception variables and applies multi-group structural equation modeling to examine how social media shapes Chinese consumers’ intentions to purchase energy-efficient air conditioning. The results show that (1) social media exposure strengthens energy-efficient purchasing intentions indirectly via behavioral attitude, subjective norm, and perceived behavioral control; (2) price perception is negatively associated with purchase intention; and (3) these effects vary by age cohort, gender, and income—Generation Z and female consumers are more susceptible to social media influence, while low-income groups exhibit heightened price sensitivity. These findings advance TPB theory and offer guidance for digital platform policies aimed at promoting energy-efficient consumption.

The development of Extended Reality (XR) technologies has reached a new advanced level today, which could be proposed as the main support in solving education problems in Indonesia. As a result, this manuscript seeks to provide an initial framework to support the idea of suggesting the most recent XR technologies as primary instruments in promoting the Sustainable Development of Learning and Education (SDLE). Based on the literature review, the old but gold Technology Acceptance Model (TAM) framework is extended with several constructs in proposing this initial framework. Several constructs proposed in this initial proposed framework are Perceived Easy-of-Use (PEOU), Perceived Usefulness (PU), Attitude Toward Use (ATU), Intention to Use (ITU), User Satisfaction (US), Learner Flexibility (LF), Self-Efficacy (SF), and Perceived Health Risk (PRH). Certainly, further research is needed to assess and determine the validity of this preliminary framework in order to serve as a guide for scientists, experts, and other stakeholders interested in integrating XR technologies for SDLE.