We studied the role of industry-academic collaboration (IAC) in the enhancement of educational opportunities and outcomes under the digital driven Industry 4.0 using research and development, the patenting of products/knowledge, curriculum development, and artificial intelligence as proxies for IAC. Relevant conceptual, theoretical, and empirical literature were reviewed to provide a background for this research. The investigator used mainly principal (primary) data from a sample of 230 respondents. The primary statistics were acquired through a questionnaire. The statistics were evaluated using the structural equation model (SEM) and Stata version 13.0 as the statistical software. The findings indicate that the direct total effect of Artificial intelligence (Aint) on educational opportunities (EduOp) is substantial (Coef. 0.2519916) and statistically significant (p < 0.05), implying that changes in Aint have a pronounced influence on EduOp. Additionally, considering the indirect effects through intermediate variables, Research and Development (Res_dev) and Product Patenting (Patenting) play crucial roles, exhibiting significant indirect effects on EduOp. Res_dev exhibits a negative indirect effect (Coef = −0.009969, p = 0.000) suggesting that increased research and development may dampen the impact of Aint on EduOp against a priori expectation while Patenting has a positive indirect effect (Coef = 0.146621, p = 0.000), indicating that innovation, as reflected by patenting, amplifies the effect of Aint on EduOp. Notably, Curriculum development (Curr_dev) demonstrates a remarkable positive indirect effect (Coef = 0.8079605, p = 0.000) underscoring the strong role of current development activities in enhancing the influence of Aint on EduOp. The study contributes to knowledge on the effective deployment of artificial intelligence, which has been shown to enhance educational opportunities and outcomes under the digital driven Industry 4.0 in the study area.

Artificial intelligence chatbots can be used to conduct research effectively and efficiently in the fifth industrial revolution. Artificial intelligence chatbots are software applications that utilize artificial intelligence technologies to assist researchers in various aspects of the research process. These chatbots are specifically designed to understand researchers’ inquiries, provide relevant information, and perform tasks related to data collection, analysis, literature review, collaboration, and more. The purpose of this study is to investigate the use of artificial intelligence chatbots for conducting research in the fifth industrial revolution. This qualitative study adopts content analysis as its research methodology, which is grounded in literature review incorporating insights from the researchers’ experiences with utilizing artificial intelligence. The findings reveal that researchers can use artificial intelligence chatbots to produce quality research. Researchers are exposed to various types of artificial intelligence chatbots that can be used to conduct research. Examples are information chatbots, question and answer chatbots, survey chatbots, conversational agents, peer review chatbots, personalised learning chatbots and language translation chatbots. Artificial intelligence chatbots can be used to perform functions such as literature review, data collection, writing assistance and peer review assistance. However, artificial intelligence chatbots can be biased, lack data privacy and security, limited in creativity and critical thinking. Researchers must be transparent and take in consideration issues of informed content and data privacy and security when using artificial intelligence chatbots. The study recommends a framework on artificial intelligence chatbots researchers can use to conduct research in the fifth industrial revolution.

In the rapidly expanding Chinese high-tech industry, high employee turnover poses a significant challenge. This study employs a mixed-methods approach to explore the association between transformational leadership and turnover intentions, utilizing both survey responses and detailed interviews. Findings from this investigation demonstrate a strong negative correlation between transformational leadership and turnover intentions. Increased job satisfaction and organizational commitment, crucial factors for employee retention, mediate this relationship. The study underscores the strategic significance for high-tech enterprises in China to nurture transformational leadership as a means to mitigate turnover, thereby fostering a more engaged and dedicated workforce, and sustaining a competitive advantage in this dynamic industry.

The mining industry significantly impacts the three pillars of sustainable development: the economy, the environment, and society. Therefore, it is essential to incorporate sustainability principles into operational practices. Organizations can accomplish this through knowledge management activities and diverse knowledge resources. A study of 300 employees from two of the largest mining corporations in South Kalimantan, Indonesia, found that four out of five elements of knowledge management—green knowledge acquisition, green knowledge storage, green knowledge application, and green knowledge creation—have a direct impact on the sustainability of businesses. The calculation was determined using Structural Equation Modelling (SEM). However, the study also found that the influence of collectivist cultural norms inhibits the direct effect of green knowledge sharing on corporate sustainable development. The finding suggests that companies operating in collectivist cultures may need to take additional measures to encourage knowledge sharing, such as rewarding employees for sharing their expertise on green initiatives, supportive organizational culture, clear expectations, and opportunities for social interaction.