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Work-related generative artificial intelligence adoption and employee-perceived organisational performance in Thailand's manufacturing industry
(2026-10-01)
Yudhistyra, Wecka Imam
;
Srinuan, Chalita
Generative Artificial Intelligence (GenAI) is increasingly integrated into organisational work processes, yet its implications for manufacturing firms cannot be inferred from adoption intention or usage frequency alone. In contrast to deterministic workplace technologies, GenAI produces probabilistic and context-responsive outputs whose organisational value depends on employees' ability to evaluate, interpret, and apply generated content effectively. This research examines how work-related GenAI use is associated with employee perceptions of GenAI-enabled organisational performance in Thailand's manufacturing sector. Drawing on the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, the Human-Centred Design paradigm, and Knowledge Management theory, the research develops a framework in which perceived usability, knowledge acquisition, knowledge application, and performance expectancy influence behavioural intention, which subsequently affects use behaviour and employee-perceived GenAI-enabled organisational performance. Survey data from 381 manufacturing employees with prior GenAI experience were analysed using Partial Least Squares Structural Equation Modelling. Bayesian Network (BN) algorithms were subsequently employed to refine the theoretical structure by identifying conditional dependencies beyond the hypothesised model, while Artificial Neural Networks and Importance-Performance Map Analysis provided complementary predictive and managerial insights. The results support all hypothesised relationships. The BN-refined model further identifies direct links between knowledge application, performance expectancy, and organisational performance, suggesting that, in addition to adoption and use, GenAI-derived value also depends on employees' capacity to translate AI-generated knowledge into workplace outcomes. The findings advance technology adoption research by developing and validating a GenAI-specific extension of the UTAUT that integrates human-centred design and knowledge management perspectives. The research further demonstrates that organisational value is realised through a sequential pathway linking adoption antecedents, behavioural intention, and use behaviour, while providing policy-relevant evidence on workforce-driven AI adoption in an emerging economy undergoing digital transformation.
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AI-powered mixed reality acceptance in mining: A PLS-SEM and Bayesian Network modeling
(2025-12-01)
Yudhistyra, Wecka Imam
;
Srinuan, Chalita
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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Adoption of industry-oriented enterprise resource planning systems: A rigorous empirical research in the mining industry leveraging PLS-SEM and artificial neural networks models
(2025-12-01)
Yudhistyra, Wecka Imam
;
Srinuan, Chalita
Enhancing operational efficiency and enabling digital transformation in the mining industry can be effectively pursued through the implementation of Enterprise Resource Planning (ERP) systems. However, there remains a significant lack of empirical research to guide successful ERP adoption within the underexplored mining sector in developing countries, contexts often characterized by harsh operational conditions, cultural resistance to change, and limited innovation. This research aims to raise awareness and facilitate effective ERP adoption by identifying, analyzing, and critically assessing the key determinants influencing ERP system uptake in the mining industry. Data were collected from 278 employees across mining organizations in Indonesia and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), complemented by Artificial Neural Network (ANN) models to improve predictive accuracy and uncover both linear and non-linear relationships. The empirical findings reveal that employee perceptions, attitudes, and organizational factors significantly shape ERP adoption behaviors. Notably, organizational size emerged as the most influential predictor, surpassing even employee attitudes and perceptions, highlighting that institutional readiness could play a more decisive role than individual user disposition. This research contributes to academic literature by offering a data-driven framework tailored to the mining industry's operational and cultural dynamics in developing economies. Moreover, it provides actionable insights for policymakers, IT leaders, and practitioners seeking to design effective technology adoption strategies that support sustainable digital transformation through ERP integration.
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Exploring the acceptance of mixed reality technology innovation among mining industry workers
(2024-11-01)
Yudhistyra, Wecka Imam
;
Srinuan, Chalita
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.
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Exploring the Acceptance of Technological Innovation Among Employees in the Mining Industry: A Study on Generative Artificial Intelligence
(2024-01-01)
Yudhistyra, Wecka Imam
;
Srinuan, Chalita
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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Extended Reality Technologies for Sustainable Development of Learning and Education in Indonesia
(2022-11-22)
Yudhistyra, Wecka Imam
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Rosyidah, A'Liyatur
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Srinuan, Chalita
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Chaveesuk, Singha
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.

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