The conservation of ceramic materials in cultural heritage faces challenges due to their vulnerability to environmental factors such as humidity, extreme temperatures and natural wear. These materials are essential in architecture and historical artifacts, requiring specialized interventions to preserve their cultural and aesthetic value. This research explores the use of artificial intelligence (AI) to optimize the diagnostic and conservation processes of ceramic materials in cultural heritage, addressing the lack of effective methodologies that combine accurate analysis with sustainable interventions.
Through a methodology based on machine learning algorithms, high-resolution digital images and data obtained from advanced sensors, such as humidity, temperature and vibration, were analyzed. The data were also complemented with environmental monitoring techniques to obtain a comprehensive view of the factors affecting conservation. The objective is to identify and evaluate the state of conservation of ceramic materials through a predictive solution that facilitates early and accurate interventions. To this end, AI models were trained to detect deterioration patterns, such as microcracks and changes in molecular structure, which are imperceptible to the naked eye.
Preliminary findings indicate that AI can identify deterioration patterns in ceramic materials with an accuracy of over 90%. These results suggest that the implementation of AI not only improves diagnostic accuracy, but also the speed of interventions, optimizing decision making in conservation processes. The ability of AI to process large volumes of data and learn from previous patterns also offers new possibilities for preventive interventions, reducing the risk of irreparable damage.
In terms of qualitative results, it was observed that the integration of AI in conservation reduces the dependence on manual and observational methods, which often limit the efficiency of interventions. In terms of quantitative results, a significant improvement in the accuracy of deterioration detection was achieved, reaching levels of more than 90% in the identification of microcracks and structural alterations.
In conclusion, the research shows that AI has great potential to transform the conservation of ceramic materials in cultural heritage, improving the accuracy and efficiency of interventions. Integrating this technology facilitates a more sustainable and scalable approach, allowing the model to be replicated in different regions without the need for specialized experts, ensuring the long-term conservation of cultural heritage.

The advent of digitization has significantly changed how we live and work, and information society services – especially intermediate services – have become crucial components of our everyday lives. Platform capitalism, which is characterized by the dominance of major digital platforms that have substantial influence across different sectors, has emerged as a result of this digital transition. The widespread impact of these platforms raises serious concerns about their societal ramifications, especially in relation to individual rights, democratic governance, and geopolitical competition. This phenomenon has given rise to the term “state platform capitalism”, where governments increasingly leverage dominant digital platforms for their own political and economic agendas.
Aspects of this phenomenon have been examined in the literature to date, but there are still significant gaps in the extant research. Specifically, further research is required to fully comprehend how platform capitalism impacts public speech, accountability, and participation as well as how the power of digital platforms interacts with democratic processes. A small number of dominant digital platforms strongly affects international politics, data sovereignty, and technological competition. Additionally, given the growing competition between China and the United States in the digital sphere and the regulatory role of European Union, the geopolitical implications of platform capitalism demand further scholarly attention.
The purpose of this paper is to fill the literature gaps by carefully examining the complex interactions between platform capitalism and the State. With this aim, and applying a qualitative methodology, this work: first, analyzes the state of the art; second, proposes a definition and identifies the challenges of the platform infrastructural power; third, discusses the main models of the relationship between dominant platforms and the State. Eventually, further research paths are suggested.

This article investigates the role of stakeholder engagement in the development and deployment of GovTech solutions, using WiseTown—an Italian City Digital Twin (CDT) ecosystem of digital solutions—as a case study. Rooted in a socio-technical perspective, the study explores how engagement with diverse stakeholders—municipal leaders and technical officers—shapes the co-design, adaptation, and scaling of GovTech solutions in real-world contexts. Drawing on a semi-structured expert interview and supported by secondary materials, the analysis explores how WiseTown’s digital ecosystem evolves through collaborative practices. The findings are structured around four key facets of stakeholder engagement: entry points and layered engagement strategies; co-design and feedback mechanisms; handling institutional resistance; and inter-municipal scaling. The study highlights a set of mechanisms—such as co-design workshops, civic tech modules, and flexible deployment models—that foster both top-down and bottom-up stakeholder engagement. These approaches enable the co-evolution of digital tools with the needs of local administrations and communities. Lessons derived from the case underscore that stakeholder engagement is not just a participatory ideal but a practical enabler for adoption, usability, and scalability. The article contributes to the GovTech literature by offering empirical and actionable insights on how engagement can overcome institutional resistance, support collaboration, and ensure long-term relevance, while enabling the design and institutionalization of inclusive, scalable, and context-sensitive public sector innovation.

The advent of generative Artificial Intelligence (AI) represents a significant challenge for contemporary copyright law. As the capacity of machines to generate content autonomously has increased, the distinction between human creation and automated production has become increasingly indistinct. This raises fundamental questions concerning the attribution of authorship and the ownership of rights. The prevailing legal framework, which is primarily anchored to the “human author” concept, is ill-equipped to resolve these issues uniformly and coherently. The intensity of the international debate has increased in recent years. Legislatively speaking, the adoption of EU Regulation 2024/1689 (AI Act) represented a significant turning point in the governance of AI. Although it does not directly regulate copyright, it introduces transparency for providers of general-purpose AI models, imposing, among other things, the obligation to document the content used for training algorithms and to comply with European copyright law, with particular reference to EU Directive 2019/790. Concurrently, the Italian legislator recently approved DDL no. 1146/2024, which proposes to extend copyright protection to works created with the help of AI, provided they are the result of human intellectual work.

In recent years, the issue concerning digital security has become increasingly important. Academics within an interdisciplinary approach are debating the topic and how to ensure and implement digital information security from the perspective of both companies and consumer users. The issue affects both the public and private sectors and requires coordinated and effective policy action.
The risks associated with such attacks are closely linked to identity theft, cyber fraud, and money laundering.
In order to address the security-related emergency, the European legislator has enacted several legislative acts. The purpose of this paper is to explain, through a legal analysis, the current regulatory framework examining the new developments and identifying any remaining gaps.
From this analysis, it appears that the topic has recently been the subject of several legislative actions. In this scenario, the NIS 2 Directive (Directive EU 2022/2555) represents a crucial update in the European Union’s legislation for network and information security. This work points out the lack of remedies for individuals in the case where the parties addressed by all regulatory interventions fail to respect their obligations.
At the same time, there is no doubt that cyber incidents have a significant impact on the profits of companies. In this regard, the Corporate Sustainability Reporting Directive (CSRD 2022/2464) introduced the inclusion of cybersecurity information in the annual report as non-financial information. For this reason, it is useful to investigate how the cybersecurity obligations contribute to the quality of sustainability reporting.
To address the application problems of these regulations, in the final part of the paper, it could be proposed to use blockchain tools to implement the certainty of digital identity and the integrity of the entered information.

The increasing adoption of artificial intelligence (AI) and robotics is leading to a profound transformation of the healthcare sector (Raimo et al., 2022), affecting diagnostic accuracy, optimising clinical processes and redefining care practices (Comite, 2022). Collaboration between healthcare professionals and intelligent machines is a key element in the evolution of care, redefining roles and changing traditional organisational structures (Çetin, 2024).
However, large-scale implementation is hampered by acceptance barriers, behavioural resistance and ethical and regulatory issues. This integration presents an opportunity to increase efficiency and reduce the margin of error (Babashahi, 2024), yet questions remain about safety, reliability and the impact on professional skills (Nivethithitha et al., 2014). Adoption depends largely on the trust healthcare professionals place in these technologies, transparency in algorithmic decision-making, and the availability of resources and adequate training.
The study asks: “How does human-computer interaction affect the acceptance of digital technologies by healthcare professionals and what strategies can be implemented to improve their adoption?”
To answer this, the authors conducted a systematic literature review (Massaro et al., 2016), analysing articles from Web of Science (WoS) and Scopus. The selection process combined automated and manual searches, identifying 11 relevant studies. Inclusion criteria focused on works addressing acceptance factors, barriers, and implementation strategies of AI and robotics in healthcare. Technical studies without a human-computer interaction perspective were excluded. Recent literature also highlights the growing role of AI-powered assistants, where human-chatbot interactions redefine service delivery (Akpan et al., 2025).
The aim is to create a strategic agenda for adopting AI and robotics in healthcare, identifying strategies and methods, including empirical ones, to facilitate integration and improve perception among professionals.
Findings show that acceptance depends on trust, perceived usefulness, ease of use and training adequacy. Resistance stems from fear of a diminished human role, regulatory uncertainty and ethical concerns linked to automation. Effective strategies include transparency in decisions, involving healthcare workers in development, and specific training programmes.
Human-computer interaction is redefining healthcare, promoting a collaborative model between professionals and AI. For this transition to succeed, organisational, ethical and training challenges must be addressed. Institutions must invest in training and policies for responsible and sustainable integration (Murugan et al., 2024). Recent studies also emphasize AI-driven assistants as tools to enhance collaboration and bridge gaps in professional-patient interactions (Liu et al., 2024). The adoption of AI and robotics (Kaur, 2024) offers a unique chance to improve care, but requires a strategic, interdisciplinary approach. This contribution offers a foundation for future research and for defining effective, sustainable implementation models.

This study conducts a bibliometric analysis of the scientific literature addressing the intersection between Specific Learning Disabilities (SLDs) and Artificial Intelligence (AI), a field that has been gaining increasing relevance due to the transformative potential of AI-based interventions in education. SLDs, including dyslexia, dysgraphia, and dyscalculia, significantly impact learning processes and academic outcomes, making it crucial to explore innovative approaches for their early identification and personalized support. AI technologies, such as machine learning, natural language processing, and adaptive systems, offer promising tools to enhance educational inclusion and tailor interventions to individual learning needs. By leveraging data-driven methodologies, AI can provide dynamic and responsive learning environments, improving both engagement and outcomes for students with diverse cognitive profiles.
Using data retrieved exclusively from the Scopus database and analysed through the Bibliometrix R package, this research examines publication trends, citation patterns, international collaboration networks, and thematic evolutions within this interdisciplinary domain. The analysis reveals a substantial increase in research output since 2020, highlighting a growing academic and technological interest in leveraging AI to address SLD challenges. Countries such as the United States, Spain, Italy, and India emerge as key contributors, fostering an expanding global collaboration network. These international partnerships reflect a shared commitment to addressing the complex and multifaceted nature of learning disabilities through cross-disciplinary research efforts.
Thematic mapping identifies core topics like dyslexia, machine learning, and personalized learning systems, alongside emerging themes such as contrastive and adversarial machine learning approaches, which represent innovative frontiers for future exploration. In particular, these advanced AI techniques show potential in enhancing diagnostic precision and developing more adaptive educational tools. Despite this promising landscape, the study underscores the need for broader empirical validation and interdisciplinary cooperation involving educators, AI researchers, and healthcare professionals. Meta-analytic evidence also suggests the importance of integrating cross-disciplinary insights when developing AI tools tailored to the needs of students with disabilities (Zhang, Carter Jr., Liu, & Peng, 2024).
The findings emphasize the strategic role of AI in promoting educational equity and suggest future research directions to consolidate and expand this field, ultimately contributing to more inclusive and effective learning environments for individuals with SLDs. Such efforts are essential to bridge existing gaps in access to tailored educational resources, ensuring that technological innovation translates into tangible benefits for learners worldwide.

This paper investigates how regulatory frameworks and financial incentives influence stakeholder engagement and the institutionalisation of grassroots innovations within the New European Bauhaus (NEB) initiative. Launched by the European Commission as a creative and interdisciplinary extension of the European Green Deal, the NEB aims to foster more inclusive, sustainable, and aesthetically engaging urban environments. Despite its transformative ambitions, limited empirical evidence exists on how NEB projects evolve from local experimental niches into institutionalised practices embedded within mainstream governance systems.
Building on Transition Theory and the Multi-Level Perspective (MLP), this study conceptualises NEB projects as niche innovations operating within broader socio-technical regimes. It explores how policy instruments shape the dynamics of stakeholder engagement, knowledge diffusion, and adaptive governance. By integrating governance studies into the MLP framework, the paper addresses a critical gap in the sustainability transitions literature: the institutional conditions necessary to scale bottom-up initiatives. Methodologically, the study adopts a qualitative embedded case study design. Data were collected through an online self-assessment form distributed to all NEB prize finalists, complemented by secondary document analysis and semi-structured interviews with project leaders across diverse socio-political contexts. The findings reveal that regulatory incentives are key enablers of stakeholder engagement, particularly when supported by strong institutional backing, clearly defined roles, and mechanisms for continuous co-creation. However, the effectiveness of these incentives is uneven. Ambiguities in policy implementation and disparities in resource accessibility limit participation for smaller or less institutionally connected actors. Some projects succeed in aligning local stakeholders around shared goals, facilitated by municipal support and EU funding, while others struggle with bureaucratic barriers and limited access to technical expertise.
Theoretically, the study extends the MLP by embedding stakeholder co-creation and adaptive governance mechanisms into transition analysis. Empirically, it provides one of the first grounded assessments of how NEB projects scale and institutionalise within multi-level governance structures. Practically, the research offers policy recommendations aimed at strengthening long-term engagement mechanisms, improving the accessibility of funding tools, and fostering cross-sectoral collaboration between local authorities, civil society, and industry.
Ultimately, this study contributes to a more comprehensive understanding of how participatory governance and regulatory design intersect in shaping urban sustainability transitions. It underscores the need for flexible, iterative governance models that allow experimental initiatives to influence policy landscapes and highlights the importance of inclusive and sustained stakeholder engagement for the institutional success of visionary policy agendas such as the NEB.

The crowdfunding phenomenon has grown exponentially over the years; however this type of financing mechanism does not only include successful initiatives, but there are also numerous cases of failed campaigns in which the proponents do not receive any funding. While most of the studies focused on the post-campaign phases of successful initiatives, a very limited number of scholars have explored the case of unsuccessful initiatives. This study is one of the first to focus on the latter and try to shed some light on the dynamics of the initiatives that have faced such a failure. We conducted qualitative and inductive research based on interviews with proponents of failed CF campaigns in the Italian context and secondary data (in particular from websites, social media, updates). The preliminary findings highlight that significant differences exist between the initiatives failed in the reward-based CF context and the equity-based CF context, as well as the stage of development of the business. Most of the interviewees highlighted the significant learning opportunity for them and the importance of creating collaborations with different stakeholders; at the same time, they faced different emotional challenges in such a scenario. This study could have useful implications for theory and practice. Among the main stakeholders involved are entrepreneurs, CF platforms, investors, policy-makers, governments, incubators/accelerators.

The textile industry faces increasing scrutiny for its environmental pollution, waste of water and energy resources, labor issues, and inefficiencies throughout the value chain (Pedersen and Gwozdz, 2014). As global agendas, such as the United Nations Sustainable Development Goals (SDGs) and the European Green Deal, call for urgent transformation, technology emerges as both a potential enabler of sustainability and a tool to mitigate the textile industry impacts. However, existing literature remains fragmented, often analyzing technologies in specific operational conditions or focusing on a singular dimension of sustainability. By conducting a systematic literature review of recent academic contributions (2020–2024), this study examines how technologies support the environmental, economic, and social pillars of sustainability in the textile industry. The review identifies and categorizes 88 articles from the Scopus database, revealing a diverse yet disconnected research landscape. From the content analysis, technologies are grouped into three clusters: emerging, established, and process-oriented. To offer an integrative perspective, the paper introduces a multi-level taxonomy that links technologies to specific sustainability-related weaknesses across the textile value chain. Findings show that while some technologies, especially Blockchain and data-driven systems, demonstrate convergent sustainability benefits, most generate partial or domain-specific improvements, such as Artificial Intelligence (AI) which raises concerns related to equity and digital exclusion (Park et al., 2020). The social dimension remains the least addressed, emphasizing the need for comprehensive strategies and governance frameworks to guide equitable technological adoption. This research offers a structured framework to help vertically integrated textile companies understand how technology adoption can address their sustainability challenges and provides practical insights for aligning business practices with innovation and SDGs objectives.

Big data technologies are fundamental in managing large volumes of data and transforming data into information. However, the mere availability of technological resources is not sufficient to extract meaningful insights. The maturity of these technologies and users’ perceptions of their use play a crucial role in determining their potential for value creation. This study proposes a maturity model that integrates both the technological maturity perceived by users and users’ own maturity in utilizing these tools. Focusing on three major cloud-based services for big data, namely Google Cloud Platform, Amazon Web Services, and Microsoft Azure, the research explores user perceptions regarding the capabilities and usability of these platforms in supporting big data initiatives. The research methodology involved the design of a structured questionnaire aimed at investigating two main constructs: “Technological Maturity”, referring to the extent to which the technology meets user needs; “User Maturity”, concerning users’ competence, awareness, and confidence in utilizing the investigated platforms. The questionnaire was administered to big data professionals, who were asked to evaluate one or more technologies based on their own experience. The analysis revealed significant trends, including a general increase in perceived technological maturity with growing user experience, and a non-linear relationship between experience and user maturity. The high number of “Don’t know” responses among less experienced participants, and their decrease among more experienced users, highlights a progressive increase in self-awareness and knowledge acquisition over time. Notably, evidence of the “Dunning-Kruger” effect emerged, indicating that less experienced users tend to overestimate their abilities. This research proposes an innovative framework for assessing technological maturity in the big data context. The model not only enhances academic understanding but also offers practical guidance for organizations in the selection, adoption, and integration of cloud technologies, while supporting effective strategies for internal skills development. The findings underscore the importance of considering, alongside technological characteristics, human factors related to perception and usability of the adopted technologies.

Industrial organizations are increasingly investing in technology and digital transformation (DT) initiatives to enhance their digital maturity and achieve sustainable growth within VUCA markets. However, the potential benefits of these investments vary depending on the company’s level of technological maturity. Higher maturity generally provides better positions to implement DT strategies and maximize their return on investment (ROI). However, due to the inherent complexity of DT initiatives, many organizations struggle to determine the most effective investment paths and align them with strategic objectives. ROI in DT is multifaceted and not limited to financial outcomes. Economic advantages can come from specific strategic and operational actions.
Digital Transformation Maturity Models (DTMMs) are proposed in literature for assessing and measuring, based on specific dimensions and factors, the business readiness in managing DT investments. Despite DTMMs are widely discussed in literature, none of them refers to ROI concept. Addressing this research gap, through a Systematic Literature Review, the study aims to explore the relationship between DT maturity and ROI and to identify the key factors facilitating the implementation of specific actions for gathering high ROI. Based on 35 DTMMs, the paper identifies 51 key factors that influence successful DT implementation and capitalization. These factors are grouped into four dimensions: People, Organization & Culture, Information & Technology, and Operations. Furthermore, through the interaction of an IT company, the study provides an original framework matching all factors with specific forms of ROI. The framework represents a valuable indication for companies to shape their digital strategies, consider DT investments, and generate meaningful ROI.

The advent of low-code and no-code (LCNC) platforms has revolutionized software development, making it accessible to users with different programming skills. These methodologies provide both technical and non-technical users with innovative solutions to optimize the software development process. The goal of this paper is to inform and enrich both scholarly discourse and practical application by synthesizing theoretical frameworks with practical experiences, ultimately driving advancements in software engineering methodologies and helping individuals and organizations to leverage these innovative development paradigms effectively. The paper presents a comparative analysis of features and capabilities of the platforms AppSheet, PowerApps, and Bubble.io. It explores Model-Driven Development’s role in enhancing these platforms. To further enrich the analysis, a group of PhD students with different expertise in software engineering and LCNC paradigms were interviewed to gather their perspectives on the practical use and potential of these platforms. Their insights provided a valuable layer of expertise to the evaluation process, highlighting nuanced challenges and opportunities that emerge in applied settings. The three platforms were systematically compared and ranked using the analytic hierarchy process (AHP), a multi-criteria decision-making methodology that structures the decision-making process into a hierarchical model. AHP’s systematic approach ensures that both subjective preferences and objective data are integrated, making it an effective method for selecting the most suitable LCNC platform. This approach allowed for an objective evaluation based on key factors such as usability, scalability, and adaptability to various development needs. By combining hands-on experimentation with expert insights and a rigorous evaluation framework, this study bridges the gap between theoretical understanding and practical application.

In today’s complex environments, organizations increasingly rely on their ability to manage change and build resilience to sustain long-term competitiveness. This phenomenon is particularly evident in high technological intensive companies dealing with frequent changes due to digitalization and technological disruptions. Through a Systematic Literature Review this study investigates the interconnections between change management, individual resilience, and organizational resilience, with the aims to identify theoretical models and key constructs shaping the complexity of business organizations when approaching to change processes. Based on the analysis of 46 peer-reviewed journal articles published between 2007 and 2025, findings reveal that individual resilience – defined as the psychological capacity to cope with, adapt and recover from adversity – plays a pivotal role in shaping employee responses to organizational change. Transformational leadership and resilient organizational cultures act as enablers, reinforcing individual capabilities and translating them into collective adaptive capacity.
Several studies draw on theoretical perspectives such as dynamic capabilities, psychological capital, and the resource-based view, suggesting that resilience is not a static attribute but a dynamic, multilevel process involving learning, improvisation, and innovation. Despite growing scholarly attention, the literature remains fragmented, with limited longitudinal designs and scarce integration across micro, and macro levels.
To address these limitations, on top of the literature, the study introduces a conceptual model that links individual resilience to organizational resilience through change management processes, in which leadership and culture represent key enablers. This model offers a strategic lens to understand how individual psychological resources can be leveraged to support organizational transformation and long-term sustainability in volatile and knowledge-intensive contexts. This study also highlights the importance of connecting human dynamics with organizational agility and encourages future interdisciplinary research on multilevel resilience as a knowledge-based capability to enable adaptive and sustainable change.

Innovation measurement remains a persistent challenge for organizations, especially in fast-evolving and complex sectors like ICT and IT, where traditional evaluation models struggle to capture the dynamic and intangible nature of innovation. Despite the abundance of proposed metrics and frameworks, the field remains fragmented, lacking a unified theoretical foundation and consistent methodological standards. This study addresses these gaps through a bibliometric analysis of 688 peer-reviewed articles published between 2001 and 2025, retrieved from Scopus and Web of Science. The analysis explores the evolution of scholarly production, identifies the intellectual roots of the field, such as the Resource-Based View, Dynamic Capabilities, and Absorptive Capacity, and maps the conceptual structure around key themes including open innovation, green and sustainable innovation, human capital, and knowledge management. The findings reveal a growing but dispersed research landscape, characterized by weak citation networks and parallel, often disconnected, research trajectories. Beyond describing the structural properties of the field, the study highlights the emergence of distinct thematic clusters aligned with different strategic and organizational perspectives. While this diversity reflects the complexity of innovation itself, it also underscores the need for more integrative and context-sensitive approaches to innovation measurement. By offering a comprehensive and data-driven overview of the literature, this study lays the groundwork for a forthcoming systematic literature review aimed at consolidating existing knowledge and supporting the development of robust, multidimensional frameworks. The results offer theoretical insights for advancing the field and practical implications for managers and policymakers seeking more effective tools to evaluate and guide innovation efforts.

Given the dynamic changes in the regulatory environment, doing business in a global network (e.g. shipbuilding network) and the increasing use of voluntary certifications and standards, companies need effective tools to monitor and make continuous improvement. A maturity model for cruise shipbuilding network from a quality management and sustainability point of view is the focus of this article. Our research question is: What quality management maturity models, that take sustainability into account in supply network context, can be identified in the literature? This is work in progress as we are developing the maturity model structure searching performance theme areas and key performance indicators for the cruise ship building network. This article is based on a preliminary literature review with themes related to quality management and sustainability. Literature was collected from Scopus AI database in January 2025. The challenge is to develop a maturity model for the network context as the most of the articles are focused on one company maturity perspective.

In the face of accelerating digital transformation, organizations are increasingly recognizing soft skills—such as emotional intelligence, adaptability, communication, and critical thinking—as essential assets for sustained competitiveness and innovation. While Artificial Intelligence (AI) is widely used to automate technical tasks, its emerging role in supporting soft skills development remains underexplored. This study investigates how AI is currently being adopted to enhance soft skill training in organizational contexts, identifying both the strategic opportunities and critical limitations involved. Drawing on a structured survey of 197 Italian firms, the research provides empirical evidence of a positive and statistically significant relationship between AI adoption in training programs and perceived improvements in employees’ soft skills. Technologies such as adaptive learning platforms, AI-powered feedback systems, and virtual simulations are increasingly used to personalize learning pathways and support cognitive and relational competencies. However, the study also highlights several challenges, including cultural resistance, ethical concerns, and the risk of reducing complex human skills to measurable behaviors. The findings suggest that AI can serve as a powerful enabler of soft skill development, but only when integrated into human-centered learning models that preserve the experiential, contextual, and social nature of these competencies. By bridging the gap between theoretical discourse and organizational practice, this study contributes to the literature on AI-enabled learning and offers actionable insights for HR professionals and corporate decision-makers. It emphasizes the need for hybrid training ecosystems where machine intelligence augments, rather than substitutes, human interaction and growth.

The increasing complexity of sustainability challenges and the growing adoption of circular economy models have reinforced the pivotal role of Life Cycle Assessment (LCA) as a strategic decision-support tool. However, the technical demands and resource intensity of LCA often hinder its widespread implementation across sectors. Recent advancements in generative artificial intelligence, particularly through large language models such as GPTs, offer promising avenues to enhance LCA practices by facilitating knowledge retrieval, standardizing processes, and supporting analytical reasoning. While early reflections have acknowledged this potential, existing research remains largely conceptual, with few empirically grounded frameworks assessing the operational viability of GPTs in LCA contexts. This study addresses this gap by developing a structured methodological framework aimed at identifying, selecting, and preparing GPT-based tools for their future application in LCA consultancy. A sample of GPTs relevant to sustainability domains was systematically mapped, and a standardized set of prompts was designed to simulate critical decision points throughout the LCA process. These prompts were refined through expert consultation to ensure methodological robustness and alignment with ISO standards. Preliminary findings suggest that, although GPTs vary widely in scope and depth, a subset demonstrates notable potential in supporting methodological structuring and inventory development tasks. By laying the groundwork for a rigorous and replicable evaluation protocol, this study advances the empirical integration of artificial intelligence in environmental modelling. It also offers practical insights for researchers and consultants seeking to responsibly harness generative AI to scale and strengthen life cycle thinking in the circular and digital transitions.

The study aims to explore the distinctive features of blockchain technology that facilitate the transition to a circular economy, which enables the measurement of circularity practices implemented by companies, providing an objective basis for their reporting and subsequent comparisons (Balzani and Corsi, 2024). The research provides an overview of the current knowledge on how blockchain technology and the most advanced digital technologies contribute to transforming from a linear economy to a circular economy. Following the Tranfield et al. (2003) guidelines, the paper develops a systematic literature review. The review highlights selected papers’ bibliometric characteristics (year of publication, document type, study approach, research country) and the main findings. The researchers summarized the research gaps, detecting the sector’s potential implications and relevant insights. The findings highlight how introducing blockchain technology has brought significant benefits to achieving circular economy practices for companies. Blockchain technology’s significant contribution to the circular economy applies to a wide range of industrial sectors. However, companies need to master new data analysis technologies to adopt blockchain technology and optimize sustainability practices properly. Blockchain links producers with commercial channels, ensuring traceability, transparency, authenticity, increased productivity, reliability, and trust-based agreements while, thanks to the immutability of recorded data, preventing fraud and information falsification (Kumar and Chopra, 2022). The study provides valuable insights for policymakers, companies, and researchers to promote the integration of blockchain technologies within businesses, contributing to the pursuit of sustainable development.

Artificial intelligence is becoming increasingly important, with applications varying from sector to sector due to its adaptability. Its most common uses range from generating clean energy to monitoring data to analyse the surrounding ecosystem and mitigating pollution damage. In the circular economy, the potential of AI could be harnessed to accelerate sustainable transactions by integrating it into design, business models and infrastructure optimisation. Thanks to its generative capabilities, AI can significantly contribute to reducing the impact of human activities on the environment by addressing challenges related to unsustainable production. In this respect, the circular economy proposes models that contribute to value creation through cycles of reuse, restoration and renewability.
This work, therefore, aims to investigate the state of the art of scientific research in the area of the circular economy in general and, with a specific focus on applications in the context of the Blue Economy, highlighting the uses, advantages and opportunities that the use of these systems brings to the Blue Economy sectors. The methodology adopted consists of a systematic literature review (SLR), aimed at categorising existing scientific contributions extracted from the Scopus database, analysing the main research trends and identifying the most relevant practical applications, and identifying future insights to encourage the adoption of AI within the Blue Economy. The results of this study highlight the limited presence of studies on the application of artificial intelligence in the context of the Blue Economy, and research is fragmented and spread across different disciplinary areas. However, the maritime sector has stood out for its strong interest in technology adoption from a scientific research perspective. This study, therefore, provides theoretical implications for academics by identifying the knowledge gap and providing guidelines for future research, and managerial implications for all stakeholders in the blue economy who want to know how to integrate the use of AI into their processes. Finally, the focus on this as yet under-researched area lends the study a character of originality and scientific relevance.