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In this research, we seek to understand the effects of artificial intelligence (AI) and knowledge management (KM) processes in enhancing proactive green innovation (PGI) within oil and gas organizations. It also aims to investigate the moderator role of trust and sustainability in these relationships.

This paper employs a quantitative analysis. Surveys have been gathered from the middle-line managers of twenty-four oil and gas government organizations to evaluate the perceptions of the managers towards AI, KM processes, trust, sustainability measures and proactive measures toward green innovation. Analytical and statistical tools that were employed in this study, including structural equation modeling with SmartPLSv3.9, have been used to analyze the data and to examine the measurement and structural models of this study.

The study results reveal a significant and positive impact of AI utilization, KM processes and PGI within oil and gas organizations. Furthermore, trust and sustainability turn out to be viable moderators affecting, and influencing the strength and direction of AI, KM and PGI relationships. In particular, higher levels of trust and more substantial sustainability commitments enhance the positive impact of AI and KM on green innovation outcomes.

Understanding the impact of AI, KM, trust and sustainability offers valuable insights for organizational leaders and policymakers seeking to promote proactive green innovation within the oil and gas industry. Thus, organizations can increase the efficiency of sustainable product development, process improvement and environmental management by using robust AI technologies and effective KM systems. Furthermore, fostering trust among stakeholders and embedding sustainability principles into organizational culture can amplify the effectiveness of AI and KM initiatives in driving green innovation outcomes.

This study extends the current knowledge by assessing the effect of AI and KM on proactive green innovation while accounting for trust and sustainability as moderators. Utilizing quantitative methods offers a nuanced understanding of the complex interactions between these variables, thereby advancing theoretical knowledge in the fields of innovation management, sustainability and organizational behavior. Additionally, the identification of specific mechanisms and contextual factors enriches practical insights for organizational practitioners striving for a practical understanding of the dynamics of the complexities of sustainable innovation in an AI-driven era.

This study aims to consolidate existing research on digital technology in business education, focusing on adoption determinants, theoretical frameworks and implementation challenges.

A hybrid literature review was conducted. The first phase involved a keyword co-occurrence analysis of 254 Association of Business Schools-ranked journal articles from the Web of Science database to identify key research themes. To deepen the analysis, the second phase included a content analysis of the 24 most globally cited articles, each with a minimum of 65 total global citations.

The review reveals that studies primarily focus on factors influencing digital technology adoption, including institutional support, perceived usefulness and ease of use. Researchers frequently cite theoretical models such as the technology acceptance model and the unified theory of acceptance and use of technology. Identified challenges include concerns over technology effectiveness, resistance to change and the influence of cultural and generational differences on technology use.

The findings guide educators and policymakers in formulating strategies for effective technology adoption, emphasizing alignment with educational goals and addressing cultural barriers.

Understanding generational and cultural impacts enables the creation of inclusive digital education strategies that cater to diverse student needs.

This study synthesizes fragmented literature, offering new insights into the integration of digital technologies in business education.

This study aims to examine the relationships among structured-on-the job training (ST), mentoring, job rotation and the work environment factors on tacit knowledge transfer from training.

This study used quantitative research techniques to examine the causal relationships among the key study variables. A questionnaire-based survey has developed to evaluate the research model by drawing a convenience sample includes 239 employees working in the Arab Potash Company located in Jordan. Surveyed data were examined following the structural equation modeling procedures.

The results revealed that adapting of the ST, mentoring and job rotation in industrial firms had direct effect on the employees’ abilities to learn and transfer tacit knowledge from training to the actual work, and how these learning strategies strengthen employees’ abilities in solving work problems, improving customers’ satisfaction and quality of products and services. As well as, it affirmed the strong direct effect of work environment factors such as supervisor and peer support on the employees’ abilities to learning and transferring tacit knowledge to their jobs. However, this study showed that work environment factors have no significant mediating role on the relationship among ST, mentoring, job rotation and the employees’ abilities to learn and transfer tacit knowledge to their jobs.

The study results are opening the doors for future studies to examine the relationships among the methods of training and learning in the workplace, the work environment factors and tacit knowledge transfer from training to the jobs as prerequisites for improving the employees and organization performance. These results would be validated by conducting future research, examining larger samples of industrial companies to give more accurate data and clear explanations to the relationships among the study variables. It also suggests to replace the characteristics of work environment (supervisor support and peer support) by trainees’ characteristics (self-efficacy and career commitment) to give a better understanding to the relationships among the key study variables.

With regard to improving the employees’ competency while doing their jobs, this study developed a conceptual framework that guides managers to recognize the importance of ST, mentoring and job rotation in increasing the employees’ learning together; and giving them the chance to use the new learned experiences and knowledge to improve the organization performance and its competitive advantage. This study helps managers build a positive work environment that encourages social interaction, respect and mutual interest among employees, and increases their sense of responsibility for learning and transferring skills and knowledge to the jobs.

The training methods in the workplace go beyond immediate work performance to act as a promising tool make employees’ learning more easily and faster, and help them to transfer and retain new skills and knowledge, adapt with changing environments, build stronger relationships with stakeholders and at the same time, make the organizations ensure that employees comply with their societal goals.

The authors have noticed that large portions of the studies on training and human resources development neglected the role effect of (ST, mentoring and job rotation) on the tacit knowledge transfer from training to the jobs. Hence, these gaps in researches have motivated to develop a theoretical model that helps to examine the relationship between the two constructs. This study also suggests to examine the mediating role effects of work environment factors on the relationships among (ST, mentoring and job rotation) and tacit knowledge transfer, as well as it extends to examine the mediating role of work environment factors on transferring knowledge to jobs, attributed to the demographic variables such as gender, age, work experience and education level.