Anthropogenic greenhouse gas (GHG) emissions are recognized as the primary contributor to climate change (CC), with the agri-food system playing a significant role. About one-third of global GHG emissions stem from activities within this system, posing challenges to food productivity and security amid a growing population. Companies in the food value chain must take proactive measures to mitigate their carbon footprint. While existing literature debates mitigation and adaptation strategies, understanding these from a business perspective remains crucial. This study aims to bridge this gap by investigating the business literature on CC within the agri-food system and identifying future research trends. Through a systematic literature review, the authors conducted quantitative and qualitative analyses. A bibliometric analysis identified trends within scientific publications, followed by a qualitative content analysis to uncover thematic areas and research gaps. Publications were gathered from Scopus and Web of Science. In doing so, the research highlights contemporary strategies to understand and tackle CC challenges throughout the food value chain, identifying different practices for each stage of the agrifood value chain, namely i) farming and agriculture, ii) production, iii) distribution iv) consumption and finally v) the whole value chain, offering a holistic perspective on the subject. The research offers practical insights for sustainable strategies by integrating environmental and strategic management principles. Additionally, this article contributes to understanding CC challenges in the agri-food system, highlighting current trends and suggesting future research directions. In particular, the research findings suggest that to effectively mitigate and adapt to CC in the agri-food industry, a holistic approach is crucial. This involves measuring sustainability across various dimensions, integrating technological and managerial measures (e.g. LCA), and considering the entire value chain. Engaging consumers in mitigation and adaptation strategies is essential. By integrating environmental and strategic management, transformational change towards sustainability can be achievable.

In the increasingly urgent scenario of sustainable development, financial institutions are emerging as key players in the advancement of the Sustainable Development Goals (SDGs) outlined in the 2030 Agenda. As such, considering the role that banks play in the economic landscape, it is crucial to clarify how they support the above-mentioned objectives. While existing literature has primarily focused on the disclosure features of financial institutions related to SDGs, there is a lack of studies examining the determinants of SDG performance. This gap underscores the need for in-depth investigations into the impact of bank characteristics on the effectiveness of SDGs achievement. Hence, this study aims to bridge this gap by delving into the extent to which the financial characteristics of banks impact SDG performance. To this end, a panel analysis was conducted on a group of global banking entities. The sample used consists of 646 publicly listed banks in 61 countries, considering the period from 2018 to 2023. Empirical results show that larger and less profitable banks reach better performance in terms of contribution to SDGs. The level of leverage does not appear to be a significant influential factor in fostering the support of the SDGs. By identifying the specific financial characteristics that contribute to SDG performance, this study provides theoretical implications related to the role of financial institutions in promoting sustainable development.

Recent developments of Generative Technologies in the field of Artificial Intelligence (GenAI) have demonstrated substantial progress, particularly in the area of participating in a human driven dialogue in a meaningful manner and of representing knowledge without explicit modelling of knowledge, capturing its implicit and tacit nature as well. These developments open a wide range of new applications and for the first time in the development of IT-supported Knowledge Management (KM) it appears to be the case that GenAI is getting suitable for knowledge intensive tasks that were reserved for humans until now. In the light of these developments the established SECI model is being investigated with respect to the impact that GenAI could have on each of the four sectors and how human user and machines can interact in novel ways under the paradigm of the SECI model. Finally, some suggestions are made to enhance the model in order to reflect the current developments and maintain the expressive power of SECI in the age of Generative AI.

This research paper focuses on examining the impact of cloud computing on knowledge processes and innovation in logistics within Industry 4.0. Using a quantitative literature review methodology, the study explores existing literature from 2019 to 2024. The results reveal fluctuations in cloud computing adoption in logistics, with notable growth in recent years. Cloud computing applications are widespread, particularly in supply chain management, transportation, and warehousing. Findings show that cloud computing significantly impacts knowledge processes and innovation in logistics. It enables real-time data processing, enhances decision-making, and supports collaboration across the supply chain. Moreover, cloud technologies foster Smart Logistics, leveraging IoT, AI, and automation for efficiency and sustainability. Despite these benefits, limitations include the reliance on literature review methodology and the rapidly evolving nature of technology. Future research should employ empirical methods to capture practical insights and address emerging challenges, such as data security and sustainability. In conclusion, cloud computing offers substantial benefits for logistics within Industry 4.0, driving efficiency, competitiveness, and innovation. However, addressing challenges and staying abreast of technological advancements are crucial for maximizing its potential in logistics operations. This study contributes to a deeper understanding of the transformative role of cloud computing in logistics and sets the stage for further research in this dynamic field.

This paper investigates the evolution of financial advisory systems from traditional roboadvisors to autonomous financial agents driven by Large Language Models (LLMs). As the financial industry increasingly embraces artificial intelligence and natural language processing, the role of LLMs in providing sophisticated, context-aware financial advice is gaining prominence. The study explores the development of autonomous financial agents, focusing on their capabilities, limitations and the impact of prompt engineering on their performance.

Within the Extended Partnership “CHANGES”, a project funded by the NextGeneration EU initiative, the University of Roma Tre and Coopculture collaborate to develop an innovative framework for the management and governance of cultural assets. This research endeavor aims to establish a methodological approach that leverages scientific data on cultural heritage to facilitate knowledge acquisition, cultural enrichment, and societal revitalization. The overarching goal is to create value and maximize impact for stakeholders, with a particular emphasis on benefiting citizens. The application of the SoPHIA model within the “CHANGES” framework is a focal point of this collaboration, specifically tailored to the revitalization efforts in the Celio District, situated in the heart of Rome. The SoPHIA model, devised to overcome the limitations of traditional evaluation methodologies, facilitates a nuanced understanding of the diverse impact areas associated with cultural initiatives. These impacts encompass empowerment, social cohesion, well-being, and the engagement of emerging publics and communities. The selection of the Celio District as a pilot case study stems from its rich cultural heritage, spanning various historical periods. However, the district also faces the adverse effects of over-tourism, owing to its proximity to major cultural attractions. This paper presents the initial findings of our ongoing research project, aimed at elucidating the contextual intricacies and cultural dynamics inherent in the Celio District. Through an analysis of data gathered from semi-structured interviews, we identify potential roles that stakeholders can adopt to foster a collaborative cultural network, thereby safeguarding and enhancing the district’s heritage. While our study sheds light on the transformative potential of cultural networks in urban regeneration, we acknowledge the inherent challenges in categorizing stakeholder impacts and commitments.

Competence management plays a pivotal role in enhancing the competitiveness of organizations in today’s fast-changing business environment. In particular, the use of ontologies for the structured management of competence data has proven to be strategic in various industries (such as, manufacturing, aviation and construction) and for different industrial processes (from supply chain management to product lifecycle management). However, despite their demonstrated utility, there remains a significant gap in the literature regarding the specific implications of employing ontologies for competence management in industrial contexts. Hence, the primary objective of this study is to investigate the implications of employing ontologies for competence management in industrial settings. By conducting a Systematic Literature Review (SLR) and bibliometric analysis about this topic, we aim to shed light on the intricacies of utilizing ontologies in managing competence and identify key areas for future research. The conducted keyword co-occurrence analysis identifies six thematic clusters: “Knowledge Representation of Manufacturing Processes”, “Education for Software Engineering”, “Semantic Technologies for Decision Making”, “AI for Knowledge Management”, “Ontologies for Human Resource Management (HRM)” and “Competence Management for Industrial Innovation”. From an academic perspective, this paper provides an overview of the implication of ontologies in industrial competence management underscoring the importance of ontologies in structuring competence-related information, facilitating decision-making processes and promoting innovation within organizations. Moreover, the study reveals a growing trend of research in this area, with emerging trends reflecting the integration of ontologies with advanced technologies, such as artificial intelligence and machine learning, which would enable organizations to rapidly adapt to changes in today’s industrial environment. From a managerial perspective, instead, the study offers insights into best practices and challenges associated with the adoption of ontologies, guiding strategic decisions toward future trends and innovations in competence management.

In the last years, supplier selection, one of the most used methods on discovering best supplier or suppliers to produce or deliver products based on some restrictions or criteria, became more and more complex, trying to include various elements into model. The manufacturing and supply chain processes have changed significantly, for various reasons, such as technological evolution or COVID-19 pandemic or for a competitive advantage. Various studies showcased the benefits of Industry 5.0 on creating a strong connection between humans and robots, thanks to the Artificial Intelligence and Machine Learning algorithms functions, which replace the repetitive activities, and improves productivity. Since COVID-19 was an unexpected event which affected the entire world, the supply chain process confronts numerous problems when the global economy stopped, and when it started back, the prices increased significantly, dealing with new problems, understanding the need of multiple sources. COVID-19 changed the business’s workflow, by taking into consideration at any moment an external risk which could alter the activity, and making them more cautious, preferring to raise the acquisition costs by diversifying suppliers. Purchasing cost represents a competitive advantage, most of the companies are making a profit based on the acquisition price. Decision-making processes are sensible to numerous components, and it’s mandatory to find an equilibrium between conflicts tangible and intangible elements. Our research analyzes a client with multiple depots, having various suppliers which offer different quantities and price levels. To address this complexity, we explored multiple methods, including Mixed Integer Non-Linear Programming (MINLP), Fuzzy Logic, and the Greedy algorithm. MINLP was created to solve dynamic supplier selection problems, allocating simultaneous quantities to the selected suppliers, being able to be customized, influencing the final price. Greedy algorithm uses fractional knapsack method, being very powerful on solving large scale problems, up to 100.000 suppliers in a few seconds, with high accuracy. The Greedy algorithm was tested in a several methods for batch delivery and supplier selection in a manufacturing domain, and in the majority of cases, the algorithm provided an optimal result, demonstrating the model’s robust fit, and showcasing a high level of effectiveness. Fuzzy logic has been used to convert the preferences expressed in text into fuzzy numbers, calculating fuzzy scores, translating into crisp scores which allows to create a ranking for suppliers, being applied to operations management, psychology, and mathematics.

Starting from the continuous development we are witnessing in our daily lives, we set out to choose a current theme that explores the impact of Artificial Intelligence on society and individuals. The aim of the paper was to understand and research the influence of Artificial Intelligence Digitalization on Quality of Life, using specific variables: Performance Expectations, Effort Expectations, Usage Behaviour, in order to observe how they influence each other. We started by creating a questionnaire with the help of which we could assess the factors studied (Performance Expectations, Effort Expectations, AI Digital Technology, Usage Behaviour and Quality of Life), which we then sent to 214 respondents. Subsequently, the data was entered into Smart PLS, with which we were able to observe the proportion of variance explained by each response to a question on the variable on which it was constructed. By identifying certain hypotheses and using the Bootstrapping algorithm, we were able to observe which of the specified hypotheses do or do not influence the independent variable. The study identified a relationship between quality of life and the use of digital technologies involving artificial intelligence, with an increase in both the former variable as the latter increases and vice versa. The use of digital technologies can help us to develop human interaction or have an emotionally balanced life, but if this use is taken to extremes, it can end up being harmful. The most significant direct link could be observed between the variables Use Behaviour and Quality of Life, followed by Use Behaviour and AI Digital Technology. With our analysis, we were able to demonstrate that, due to its ability to be used in a wide range of industries, artificial intelligence can significantly contribute to improving efficiency, productivity and innovation in a wide range of industries.

In a knowledge-based economy, a gender-diverse board may enhance intellectual resources, as a driver of firms’ value creation. This study aims to explore the connection between Intellectual Capital Efficiency (ICE) and Board Gender Diversity (BGD) within Italian innovative startups. In this way, it seeks to address the literature gap concerning the existent relationship between the variables, given the growing interest in gender diversity. A quantitative analysis has been carried out on a panel dataset (2018-2022). Generalized least squares and ordinary least squares have been run to test the link between ICE and board gender diversity. The ICE, as dependent variable, is measured through the VAIC™ and each of its components (human, structural and employed capital efficiency), while two alternative proxies of board’s gender diversity are used, the Blau Index and women dummy. A set of control variables are included to account for the impact of factors that previous studies have identified as influencing the magnitude of VAIC™ and its three components. Consistent with Critical Mass Theory, Agency Theory and Resource Dependency Theory, several studies have established a connection between female participation and firm performance. However, empirical evidence continues to yield mixed results. Based on our analysis a significant and positive relationship between both variables emerges. The study’s outcomes can aid in the advancement of knowledge about gender and intellectual capital, and can be beneficial to policymakers, entrepreneurs and practitioners. Due to the specific focus of the exploration on innovative startups within the Italian economy, the research findings may not be entirely suitable to other business. Furthermore, future research should be addressed to investigate different sectors among longer periods.

The Sustainable Development Goals (SDGs) are a guide for caring for the planet, guaranteeing the fundamental rights of its inhabitants and shaping sustainable economic growth. In the current context, characterised by great challenges and geopolitical conflicts, the figure of the CEO is key to driving the necessary transformation of companies and the prioritisation of their commitment to the current challenges of the world we live in. In this regard, the aim of this paper is to deepen current knowledge on the role of CEO visionary leadership in shaping inclusive and sustainable business models aligned with the goals of the 2030 Agenda. our findings show that the information disclosed by companies in relation to projects aligned with the SDGs is positively associated with leadership figures who wield power that allows them to influence the agenda and decisions made by the board of directors. The influence of leaders is reinforced in scenarios where companies excel in sustainability performance.

The elaboration and the application of modeling and tools supporting the innovation processes and the digital transformation of the tourism organisations and of the wider tourism ecosystems require not only a review of products and services, processes, work practices and organizational and networks structures, but also a cultural evolution towards the digital-driven innovation and the creation of a system of collaborations capable to identify, select, and involve effectively all the potential stakeholders interested in elaborating and adopting smart tourism solutions. Accordingly, renewed management methods, tools and practices need to be elaborated, understood and applied. To fill these gaps, Innovation Labs modeling and participatory events are identified and analysed as potential new key-levers to stimulate and supporting the digital innovation journey and the adoption of smart tourism solutions. The methodology elaborated and used in this paper is based on a desk-analysis of scientific research sources. The main findings of the research reside in an examination of the new management methods, tools and practices required to foster digital innovation culture and the adoption of smart tourism solutions, and in a systematization of the main traits characterizing the different potential formats of the participatory events inspired by the Innovation Lab principles. The value of the article mainly lies in its attempt to identifying and clarifying relevant themes and unanswered research questions about innovation dynamics in cultural tourism industries to be effectively declined and investigated according to a relatively new lens of analysis.

Blockchain technology is set to revolutionize the agrifood industry, combining sustainability and economic performance. Blockchain based tracking systems can be developed to ensure security, accountability and reduce waste and inefficiencies in the supply chain. Despite the potential benefits, blockchain applications in the agrifood industry remain limited. Furthermore, most studies address the issue from a purely theoretical perspective, and thus it remains unclear how blockchain can help agrifood companies reconcile sustainability and innovation. In this study we help bridge this gap, adopting a multiple case study methodology to investigate how blockchain can help SMEs in the wine industry address key sustainability issues. Results suggest that blockchain can play a major role in ensuring security and transparency in the wine supply chain. This allows companies to combat fraud and counterfeiting, comply with regulations, and build trust relationships with consumers. In contrast, blockchain does not offer a significant contribution to companies’ environmental sustainability. From this perspective, high investment costs and the need to combine different technologies remain key challenges. Overall, this study contributes to the literature by providing empirical evidence of how blockchain technology might improve agrifood companies’ sustainability. Furthermore, it provides useful practical suggestions to managers of agrifood companies to assess the impact that blockchain adoption can have on the business, in terms of sustainability and economic performance.

Digital platforms are pivotal for innovation and recently for sustainable innovation processes. Platform thinking is the key to the innovation of business models in a scenario increasingly immersed in the fifth industrial revolution. Coupled with the rise of artificial intelligence (AI), they facilitate effective data utilization across various sectors. With Artificial intelligence’s potential to address Sustainable Development Goals, it’s applied in several fields. Integrating AI into digital platforms drives sustainable innovation, such as in energy management. Posing in this scenario the research aims to explore the sustainability impacts of AI-driven digital platform innovation, employing a systematic literature review methodology contributing to understand the state of the art and the future research directions about sustainable development in digital platforms enhanced by AI.

The healthcare system is currently facing challenges such as an ageing population, rising costs, and a shortage of healthcare workers. To address these issues and ensure sustainability, innovative strategies like telemedicine, lean organization, and process mining must be implemented. These strategic levers can improve efficiency, effectiveness, and patient outcomes by employing machine learning elements to predict outcomes and recommend personalized treatment plans based on individual patient data. Risk predictive ML algorithms are crucial in estimating the risk of chronic diseases, moving towards a global assessment to provide personalized recommendations for chronic care processes. By integrating risk stratification methodologies and predictive algorithms, healthcare organizations can improve health outcomes and reduce avoidable costs in managing high-risk populations while considering social data for fragile populations.In the context of chronic care processes, a modern care model based on efficient processes and the support of healthcare companies could offer assistance to vulnerable patients with complex needs. By utilizing digital tools like telemedicine, healthcare organizations can enhance the management of patients and healthcare professionals, ultimately promoting the well-being of all stakeholders. The COVID-19 pandemic has accelerated the adoption of telemedicine and digital tools, showcasing their feasibility and effectiveness on a large scale. Telemedicine plays a crucial role in reorganizing healthcare services, improving accessibility, and quality while offering prevention, education, and patient coaching programs to monitor health indicators and reduce complications.Telemedicine, connected platforms, and ML algorithms are essential in modern healthcare systems to enhance patient-centered care processes, improve coordination between stakeholders, and integrate social and health services for better continuity and quality of care. By leveraging telemedicine, healthcare organizations can optimize resources, provide services close to patients, and implement preventive measures to reduce the risk of complications. The proposed methodology for designing prevention-focused PDTA leverages telemedicine and ML algorithms to predict chronic risks, optimize care processes, and improve patient outcomes. The BPMN-PMO workflow model illustrates the prevention process based on monitoring, data processing, and decision support for general practitioners to ensure the sustainability of the model through technology readiness and organizational capabilities. The integration of telemedicine, ML algorithms, and PMO models is crucial for improving the prevention and management of chronic diseases, enhancing patient care, reducing costs, and improving health outcomes. By adopting innovative technologies and optimizing organizational processes, healthcare organizations can provide personalized and efficient healthcare services for all individuals in need, paving the way for a future of collaborative care and digital healthcare solutions.

This work aims to understand how the process of selecting and managing partnerships is organized within Innovation Ecosystems. Drawing from a combination of literature review and practical examples, a tool is developed to aid decision-makers in evaluating potential partners at both national and international levels. The tool’s construction involves identifying principles from existing research and practice, leading to a comprehensive framework for assessing partnership potential across various dimensions including execution capacity, partners maturity, proof of value, co-development, IP management and valorization, and internationalization. Through a rigorous testing process involving different researchers and a diverse range of Innovation Ecosystems in different countries, the effectiveness of the tool is demonstrated, particularly in assessing partner maturity, execution capacity, and internationalization potential. However, challenges are identified, particularly in understanding and evaluating IP management policies within IEs, suggesting new pathways for further research and refinement of the tool. Overall, the theoretical and practical contributions of the research address the gap in managing partners within Innovation Ecosystems and provide concrete insights for future developments in the field.

This research, grounded on the resource-based view theory, examines the impact of developmental culture on inbound open innovation in small and medium-sized enterprises (SMEs) in Albania. Using a quantitative survey, data were collected from a sample of 179 service and manufacturing SMEs. The study demonstrates that there is a significant relationship between developmental culture and commitment-based HRM and inbound open innovation, as revealed by the analysis of quantitative survey data using PLS-SEM in SmartPLS 3.0. Moreover, commitment-based HRM serves as an intermediary between a culture that promotes growth and the process of incorporating external ideas and knowledge into an organization, known as inbound open innovation. The results highlight the necessity of transitioning towards cultivating organizational cultures that encourage collaboration and the sharing of knowledge in order to stimulate innovation. Open innovation is recognized as a crucial factor in generating value, enhancing competitiveness, and fostering economic progress. It is recommended that policymakers and industry practitioners embrace new strategies that value openness and collaboration in order to promote the dissemination of innovation and promote sustainable growth

The relevance of digital transformation (DT) is acknowledged across industries, including cultural tourism. However, existing frameworks for digital maturity in this sector are limited and fragmented. This lack of a comprehensive framework hampers the ability of cultural tourism organizations to effectively assess their digital maturity, address challenges related to DT, and formulate strategic roadmaps for digital initiatives. Therefore, this paper develops a Digital Maturity Model (DMM) tailored specifically for cultural tourism organisations, to drive them in their DT journey, towards digital excellence. The study conducts a comparative analysis of existing DMMs across various sectors to identify key dimensions and sub-dimensions relevant to the context of cultural tourism. This analysis set up the foundations for developing a comprehensive model for assessing digital maturity in cultural tourism organizations. The comparative analysis reveals seven principal dimensions consistently emerging across existing DMMs and crucial for cultural tourism organisations, specifically: Strategy, Organization, Culture, Employee, Tourists, Technology, and Operations. Each dimension is further divided and characterized into twenty sub-dimensions, providing a comprehensive framework for assessing digital maturity in cultural tourism sector. This study contributes to the literature by providing a tailored DMM specifically designed for cultural tourism organizations, addressing the unique challenges and requirements of the sector. It offers a foundation for developing DT strategic roadmaps and advancing the maturity model field further, particularly in the cultural tourism sector. The proposed DMM offers practical support for managers, employers, and other stakeholders involved in strategic decision-making within cultural tourism organizations.

The objective of this study is to delve into the challenges that female entrepreneurs encounter when seeking access to credit and to examine the strategies they employ to overcome these hurdles. A case study was conducted, involving interviews with an Italian businesswoman in the food sector. Access to credit emerges as a vital tool for enhancing the economic standing of these enterprises and fostering gender equality. Existing literature corroborates the presence of various obstacles faced by women when applying for credit, such as lower incomes and a lack of financial literacy. These barriers present a significant challenge in securing the necessary resources both for initiating and expanding their ventures. This study employs qualitative research, utilizing a pilot single case study approach to analyze real-life events (Yin, 2003). The case study method enables detailed data analysis within a specific context, exploring contemporary phenomena through limited events or conditions and their relationships (Yin, 2009). Additionally, it allows researchers to articulate the significance of social actions and events, facilitating a nuanced interpretation of their meanings. This study provides valuable insights and evidence based on the experience of a small businesswoman in the food sector. It reveals that one of the factors limiting her access to credit may stem from cultural factors, not just gender perspectives. The financial sectors and policymakers could adopt a unified approach to foster financial inclusion by designing programs for entrepreneurs in collaboration with various governmental and non-governmental institutions. From a theoretical standpoint, these contributions identify conceptual gaps in understanding credit access and its perception across contexts and regions, aiding the development of solid theoretical frameworks. Future research suggests exploring communication channels between banking institutions and female entrepreneurs, as well as financial products tailored to small businesswomen.

Expertise Locator Systems (ELS) are recognized as key tools in knowledge management programs. However, they face significant challenges, such as maintaining expert profiles, knowledge representation, and evaluating their effectiveness. This research conducted a systematic literature review to explore how Large Language Models (LLMs) from generative artificial intelligence can contribute to overcoming these challenges and enhance the implementation of ELS. The findings highlight the importance of prompts, augmented information retrieval, and knowledge graphs in the development and application of LLMs. Based on these insights, we propose an architecture for ELS supported by LLMs and present a prototype to assess the feasibility of this approach. Preliminary results indicate a substantial potential of LLMs to foster the development of a new generation of knowledge-based systems.