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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.

The main purpose of this study is to determine the antecedent factors of smart government service apps intention and its impact on actual use by extending the unified theory of acceptance and use of technology (UTAUT) in the Jordanian context.

To achieve the main purpose, a quantitative method was used to test collected data, and hypotheses testing through using statistical package for social sciences (SPSS) and smart partial least squares software.

The findings indicate that social media advertising has a positive effect on both social influence and peer influence. Furthermore, it demonstrated a significant effect of social influence on performance expectancy. In addition, there is a direct correlation between the government capacity, effort expectancy, facilitating conditions and the intention to use e-government services. Lastly, the results mainly show that the actual use of e-government services is significantly and positively influenced by intention and self-isolation. Next, as expected, self-isolation moderated the relationship between intention to use and actual use of e-government services via the Sanad application, and hence the related hypothesis was supported.

This study provides practical recommendations for the policy-makers in the Jordanian e-government and Ministry of Digital Economy and Entrepreneurship (MoDEE) in Jordan.