Blockchain technology remains popular for several reasons. The main one is that it has facilitated the rise of digital currencies over the past several years and many other uses of non-crypto currency. There is a belief that the technology itself could far exceed cryptocurrencies by its impact. Thus, researchers are still discovering the real potential of blockchain. This study aims to conduct a comprehensive blockchain analysis with a bibliometric study. The data was retrieved from the Scopus database and was analyzed using the VOSviewer software, developed at Leiden University’s Centre for Science and Technology Studies (CWTS), Leiden University, the Netherlands. The study is based on the analysis of 1842 documents published in the 2007–2021 period using Scopus. From the visualization, three main groups of six clusters are generated. The red area includes topics related to blockchain technology, supply chain management, and sustainable development. The green cluster stands for such keywords as blockchains, smart contracts, electronic money, and Bitcoin and Ethereum. The blue cluster area focuses on issues related to artificial intelligence, big data, health care, and COVID-19. The analysis helps to improve the quality of the review by directing researchers to the most significant documents and mapping areas of publications.

Organizations see open innovation as important to their future growth strategy. The increasing interest in artificial intelligence has led to a heightened interest in its potential applications in many industries. Many firms invest heavily in artificial intelligence intending to innovate their business models, though managers often lack understanding when trying to implement artificial intelligence in their operations. The data was retrieved from the Scopus database and was analyzed using the R Bibliometrix Biblioshiny and VOSviewer software. The aim of the article is to indicate the consistency in the formation of open innovation processes while applying artificial intelligence and to provide the profile of perspectives on artificial intelligence adoption in innovation management. This paper provides a deeper perception of artificial intelligence and how it can be used to drive open innovation processes and business model innovation within the use of artificial intelligence in open innovation processes and artificial intelligence in the management of open innovation. The authors discuss how recent advances in artificial intelligence have created new opportunities for increased external collaboration. The study found that the rise of artificial intelligence as a key technology for promoting openness and collaboration has ushered in a new era of achievable open innovation. Our presented findings suggest the sequence of open innovation processes powered by artificial intelligence and insights into the artificial intelligence application to innovation management.

Improving energy management has received a lot of attention due to environmental issues, energy crises, and growing energy prices in today’s world. Various digital technologies have been developed to enhance energy management to cover these challenges. This article investigates Blockchain and Artificial Intelligence, which have recently attracted increasing attention. The study applies the software of VOSviewer for providing bibliometric analysis. The data (pull of the scientific documents) was generated from the Scopus and Web of Science. There have been done a critical analysis of the literature for evaluating the research in the proposed area. This paper focuses on possible use cases of blockchain and artificial intelligence technologies and considers aspects of technology used to achieve energy efficiency. The findings showed that the technologies are widely applied in energy management. Besides, it is lack of documents that contain the terms ‘energy efficiency and ‘blockchain’. The results of analysis confirm that artificial intelligence has been rapidly integrating with energy management, helping to develop more efficient and secure energy generation techniques.

Environmental management systems (EMS) are essential in promoting sustainable practices and mitigating the adverse effects of human activities on the environment. As technology continues to advance, there is an increasing opportunity to utilize advanced technologies to improve environmental management systems. This article examines the potential of different advanced technologies, such as artificial intelligence (AI), blockchain, big data, and the Internet of Things (IoT), within the context of environmental management systems. This article intends to offer valuable insights to researchers, practitioners, and policymakers by examining the potential uses of AI, blockchain, big data, and IoT in environmental management systems. The goal is to demonstrate how these advanced technologies can be leveraged to enhance sustainability, boost environmental performance, and yield favourable environmental results across different sectors and industries.

Organizations implement intelligent automation across diverse operational contexts but often lack comprehensive frameworks for strategic decision-making and cross-domain integration. The existing literature frequently examines isolated applications with limited implementation guidelines addressing environmental interdependencies. This study conducts a systematic review of 69 publications (2019–2024) using thematic analysis to examine automation patterns across six environmental domains: social, economic, educational, scientific, technological, and ecological. The analysis identifies three implementation patterns: efficiency-focused domains (economic, technological) emphasizing operational optimization; capability-focused domains (social, educational) prioritizing human augmentation; innovation-focused domains (scientific, ecological) developing transformative applications. Cross-domain analysis reveals integration opportunities and sustainability considerations. The study proposes a strategic decision-making framework incorporating environmental assessment tools, quality enhancement mechanisms, and planning capabilities. This framework supports organizations in selecting domain-appropriate strategies while addressing sustainable transformation objectives. The research provides systematic environmental categorization of intelligent automation applications and offers implementation guidelines for practitioners pursuing coordinated digital transformation across organizational contexts.

Despite widespread artificial intelligence adoption (78% of organizations), only 1% achieve mature implementation, creating a critical research challenge in understanding the disconnect between technological deployment and operational integration. This study develops a comprehensive multidimensional gap analysis framework to systematically assess AI implementation effectiveness across technology adoption, human capital readiness, financial adequacy, and strategic alignment dimensions. The methodology integrates Parasuraman’s service quality gap analysis with strategic alignment theory, employing systematic analysis of authoritative data from McKinsey, IBM, PwC, and Deloitte. Integration depth coefficients differentiate reported adoption from functional operational effectiveness, addressing limitations in traditional binary assessment approaches. Results demonstrate substantial implementation gaps across all dimensions: strategic alignment (80%), technology integration (70.75%), financial optimization (57.31%), and human capital development (16.67%). The strategic gap emerges as the most critical barrier, with fewer than 20% of organizations tracking AI performance indicators. Notably, the human capital gap significantly underperforms industry perceptions, indicating systematic overestimation of skill requirements. Statistical validation confirms the framework’s robust predictive capability, with AI implementation level emerging as the primary efficiency driver and gap reduction serving as critical enablers. Industry analysis reveals substantial implementation variations across sectors, confirming the need for tailored organizational strategies. The validated framework provides systematic assessment tools for evaluating AI implementation maturity and identifying optimization priorities. Organizations must prioritize comprehensive implementation scaling alongside strategic measurement development to realize investment potential and achieve sustainable competitive advantages in digital transformation.