New technologies, particularly blockchain and non-fungible tokens (NFTs), are profoundly transforming the cultural and artistic sectors. Museums are actively responding to this shift by adopting advanced digital tools that enable more engaging, personalized, and innovative visitor experiences. Through the integration of NFTs and immersive technologies, museums can extend the boundaries of cultural participation beyond the physical space, allowing users to interact remotely or gain access to exclusive content such as private exhibitions, supplementary materials, and virtual events. NFTs also allow museums to offer limited digital editions of artworks to collectors around the world, fostering new communities and business models based on digital ownership and participation.
These emerging opportunities demand a reevaluation of how artworks are protected, enhanced, and enjoyed in digital environments. However, for stakeholders to adopt these tools with confidence, several challenges must be addressed.
This study, grounded in the Italian legal framework, investigates key legal issues including copyright law, image rights of cultural assets, and the role of Online Dispute Resolution (ODR) mechanisms in resolving disputes that may arise in NFT transactions. Additionally, the study examines the fiscal implications linked to the creation and commercial use of NFTs by companies.
In a context marked by regulatory uncertainty and diverging legal theories, our research reviews prevailing interpretations and, pending legislative action, proposes a preliminary legal framework. This framework aims to ensure that the creation and transfer of NFTs via smart contracts can be carried out in a transparent, secure, and legally coherent manner, fostering greater trust in the digital transformation of the cultural heritage sector.

Knowledge is widely recognized as a core strategic resource in today’s knowledge-driven economy. Consequently, Knowledge Management (KM) processes have become central to organizational strategies (Yee et al., 2019; Krause et al., 2019). The spread of new technologies is driving structural changes and innovative dynamics in the business landscape. Decentralized Autonomous Organizations (DAOs) redefine traditional structures using blockchain technology (Wright, 2021). This decentralized structure provides an open, transparent and democratic platform, allowing organizations to harness collective intelligence (Kahan and Rock, 2008; Gola et al., 2023) and improve knowledge management, which all participants can access, contribute to and share information in real time. (Bhatt, 2001; Liu et al., 2022; Phillips, 2024).
While literature covers the importance of knowledge management in organizations (Jelenic, 2011; Hebibi et al., 2019; Koivisto and Taipalus, 2023) as well as DAOs and their decentralized governance structures (Piselli, 2019; Santana and Albareda, 2022; Guillaume et al., 2022), there is a gap in addressing how these two domains intersect. Currently, how a decentralized structure impacts knowledge management remains largely unexplored. This work will bridge the gap between KM practices and DAOs, highlighting this delicate balance between opportunities and challenges.
Through qualitative analysis supported by a comprehensive review of international literature and legal references, this article investigates the impact of decentralization via smart contracts and the transparency enabled by blockchain technology. On one hand, DAOs provide an environment conducive to the democratic sharing of knowledge, encourage direct participation, and reduce information asymmetries. On the other hand, the fragmentation of knowledge, loss of tacit knowledge, and absence of a clear regulatory framework present significant challenges to the sustainability of KM processes.

Research, innovation, and entrepreneurship are key in academia, with entrepreneurship serving as a bridge between science and business to drive economic growth. While being more inclusive of women is generally seen as beneficial, gender equality is still far off, with a clear gender gap showing unequal conditions. The Global Gender Gap Index (GGGI) in 2024 showed a global gender gap of 68.5% across 146 economies. The Economist’s Glass-Ceiling Index (GCI) in 2024 highlighted a persistent 12% wage gap. This study investigates the gender gap in research, innovation, and entrepreneurship across European universities, specifically examining the distribution of European Research Council (ERC) funding and potential gender bias. Analyzing the geographical allocation of ERC grants, the study reveals a funding disparity, with fewer women researchers receiving funding. While countries with fewer projects show no significant gender differences, those with larger funding volumes exhibit clear disparities favouring men. The second part of the study explores the potential reasons behind the differing percentages of female and male researchers and the socioeconomic factors influencing ERC funding. The analysis suggests that gender equality has an impact on the proportion of female Principal Investigators (PIs) in a country, and factors like women’s participation in the workforce, working conditions, entrepreneurship, and political representation influence ERC grants for women. Structural changes are so needed for a more gender-balanced academic environment. Future research could look at sociocultural and psychological barriers for women in securing ERC funding.

The study explores the under-investigated intersection between corporeality and entrepreneurship by conducting a systematic literature review on the role of physical appearance in entrepreneurial processes. Despite traditional interest in the influence of bodily aesthetics—particularly physical attractiveness—within feminist scholarship, current research is largely dominated by theoretical approaches rooted in evolutionary and social psychology. Existing studies, while methodologically rigorous, often lack socio-political depth and fail to critically engage with feminist or intersectional paradigms. By adopting a PRISMA-guided systematic review protocol review, the authors identified a final corpus of 15 core studies. Such studies were subjected to bibliometric framing and deductive qualitative content analysis aimed at identifying dominant theoretical backgrounds, recurring methodologies, sample characteristics, and investigated entrepreneurial stages. Results indicate a pronounced quantitative orientation and a consistent preference for structured entrepreneurial environments and early-stage financing scenarios. The analysis reveals a pervasive attractiveness bias and a lack of critical engagement with its implications, which may contribute to legitimizing existing gender hierarchies within entrepreneurial ecosystems. The study ultimately calls for a broader epistemological agenda that integrates feminist ethics, qualitative inquiry, and a critical perspective on bodily representation in entrepreneurship research. The findings provide a foundation for more inclusive and politically aware investigations into how corporeality shapes entrepreneurial outcomes and imaginaries.

In an era marked by ever-increasing environmental concern and technological novelty, Life Cycle Assessment (LCA) has come to serve as one key instrument for evaluating environmental performance of organizations. Conversely, much literature has neglected possible strategies for aligning digital transformation (DT) with LCA activities as digital technologies care about dissemination. This study fills the gap by examining how non-technological assets such as data-driven culture (DDC) and digital capabilities (DC) spur digital transformation in LCA practice. Building on Resource-Based View (RBV), this study sees DDC as an organizational resource and DC as a human resource that mediates the effects of DDC on DT. Data were collected through an online survey among 229 Italian companies in the agri-food sector, an environment where the institutionalization of LCA adoption occurs. Structural equation modeling (SEM) was used to test hypothesized relationships. The results suggest that while DDC has no direct effects on DT, it promotes digital capabilities among employees, which in turn affect DT, thereby confirming a full mediation effect. The research supports the idea that transformation of practice relative to sustainability needs actual digital innovation and therefore cannot be achieved by just introducing new technologies. Instead, an important aspect of this process is to bring about a cultural change and the development of human skills. As much as technology influences the advancement of sustainability schemes, such as LCA, it could be unaffecting just by itself. A data culture must grow to foster an environment where data are valued, disseminated, and applied in decision-making, whilst employees should have digital capabilities that help them to draw insights from data, take action, and innovate. Conversely, if cultural and human elements are taken into consideration, the application of the technologies will be enhanced and maximized.

The convergence of Artificial Intelligence (AI) and Circular Economy (CE) is often suggested as a promising pathway for sustainable transformation in the agri-food sector. Yet, how human involvement is configured across AI systems, and whether it enables or limits circular outcomes, remains poorly understood. This study examines how AI is integrated into CE-related practices in agri-food organisations and how human roles are distributed across the AI lifecycle, with particular attention to organisational size and structure. Based on a systematic review of 88 peer-reviewed articles published between 2015 and 2025, the study offers a cross-cutting analysis of how AI systems are conceptualised, developed, deployed, and monitored, and how CE goals are addressed within these processes. Results show a strong imbalance: the early phases of the AI lifecycle are largely shaped by external technology providers, while internal organisational actors are mostly engaged during implementation and operational tasks. Strategic and governance-related phases remain underexplored, and participatory approaches are rare. CE considerations are often reduced to operational metrics, such as waste reduction or resource efficiency, rather than driving systemic redesign or regenerative business models. These findings highlight the need to reposition human involvement as a central lever in aligning AI with circular outcomes. Without stronger organisational capacity, inclusive governance, and CE-aware design logics, AI risks reinforcing existing inefficiencies instead of enabling ecological transition. The study offers a timely contribution to debates on digital sustainability and agri-food innovation, proposing future research directions that foreground agency, adaptability, and systemic value creation.

The increasing urgency of environmental concerns, alongside the global challenges posed by climate change and resource depletion, has underscored the need for a transition toward a circular economy (CE). The banking sector plays a pivotal role in this shift, not only by financing and supporting circular initiatives across industries, but also by adopting CE principles within its own operations to enhance accountability and build stakeholder trust. In this context, banks are expected to improve transparency by disclosing information about their CE practices. Despite this growing relevance, CE disclosure (CED) in the banking industry remains an underexplored area in academic research. Therefore, this study seeks to address this gap by investigating the impact of CED on banks’ market value. Adopting a signalling theory perspective, the analysis applies an Ordinary Least Squares (OLS) regression to a sample of 107 European listed banks, focusing on CE information disseminated through their official websites. The results reveal a positive and statistically significant relationship between CED and market value. These findings offer interesting theoretical insights and practical implications.

The circular economy (CE) is increasingly being recognized as a viable alternative to the traditional linear model of production and consumption, which is now widely acknowledged as unsustainable. For these reasons, the academic literature has begun to take an interest in the way in which companies implement CE strategies in their business models and disseminate information regarding this new model of production and consumption. Specifically, scholars have begun to investigate the dissemination of CE information, but this area of research is still underexplored. In fact, there are only a few studies investigating the factors that influence the levels of CE disclosure (CED). In order to fill the knowledge gap, this study aims to examine the impact of national culture on CED levels. First, this study uses a manual content analysis to measure the amount of CE information disseminated in the sustainability reports of 81 international companies. Secondly, a linear regression model is used to test the impact of national culture on the CED. The results show adequate levels of CE information disclosed through sustainability reports. In addition, they demonstrate a negative impact of power distance and masculinity and a positive effect of uncertainty avoidance and long-term orientation on the CED. To the best of our knowledge, this is the first study to explore the influence of national culture on CED, addressing a gap in the literature at the intersection of cultural values and sustainability reporting practices. By highlighting how specific cultural dimensions shape firms’ disclosure behaviour, this study provides valuable insights for companies –seeking to align their reporting with stakeholder expectations – as well as for policymakers and training institutions, who aim to promote more effective and culturally sensitive sustainability frameworks.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.