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This research paper explores how grassroots innovation initiatives address and resolve the challenges of translating broad and inviting values such as sustainability, inclusiveness and aesthetics into practical and actionable structures. This study examines the tensions and difficulties projects face in operationalizing these values, revealing the gap between idealistic goals and real-world implementation. Moreover, this paper analyzes how role expectations and the concept of invitation ambiguity affect top down and bottom up approaches, offering insights for improving mechanisms to support grassroots innovations.

This study uses an exploratory qualitative methodology with an embedded case study design, focusing on the New European Bauhaus (NEB) and its award-winning projects. Data were collected through online self-assessment surveys, secondary data analysis, and semi-structured interviews with project owners and NEB Unit representatives.

The findings reveal significant challenges in translating broad and inviting values (sustainability, inclusion and aesthetics) into actionable outcomes for grassroots projects. Key issues include the need for clearer role definitions, tailored support, and adaptability. Conflicts between those values and a mismatch between expectations about stakeholders’ contributions highlight the need for designing more flexible and robust frameworks and robust frameworks.

This research explores the effects of invitational ambiguity within grassroots innovation, revealing how broad values ‐ like sustainability, inclusion and aesthetics ‐ are operationalized in real-world settings. By applying collective action theoretical frameworks to the unique case study of NEB projects, this study provides fresh insights into the dynamics between top-down European policies and bottom-up grassroots practices.

The purpose of this paper is to propose a framework for intelligent analysis of Twitter data. The purpose of the framework is to allow users to explore a collection of tweets by extracting topics with semantic relevance. In this way, it is possible to detect groups of tweets related to new technologies, events and other topics that are automatically discovered.

The framework is based on a three-stage process. The first stage is devoted to dataset creation by transforming a collection of tweets in a dataset according to the vector space model. The second stage, which is the core of the framework, is centered on the use of non-negative matrix factorizations (NMF) for extracting human-interpretable topics from tweets that are eventually clustered. The number of topics can be user-defined or can be discovered automatically by applying subtractive clustering as a preliminary step before factorization. Cluster analysis and word-cloud visualization are used in the last stage to enable intelligent data analysis.

The authors applied the framework to a case study of three collections of Italian tweets both with manual and automatic selection of the number of topics. Given the high sparsity of Twitter data, the authors also investigated the influence of different initializations mechanisms for NMF on the factorization results. Numerical comparisons confirm that NMF could be used for clustering as it is comparable to classical clustering techniques such as spherical k-means. Visual inspection of the word-clouds allowed a qualitative assessment of the results that confirmed the expected outcomes.

The proposed framework enables a collaborative approach between users and computers for an intelligent analysis of Twitter data. Users are faced with interpretable descriptions of tweet clusters, which can be interactively refined with few adjustable parameters. The resulting clusters can be used for intelligent selection of tweets, as well as for further analytics concerning the impact of products, events, etc. in the social network.

This work aims to assess the potential of a Bio-District as a model for applying the circular economy concerning the waste scope. It aims to understand the capability of organic farms to manage waste with a circular perspective, starting with the use of indicators that directly or indirectly impact the waste scope.

This study is based on previous work that identified and systematised the circular indicators of the agri-food sector within a dashboard. With this research as a basis, the indicators within the waste scope in the dashboard were extracted. Cross-linked indicators with an indirect connection to the waste scope were also systematised and tested in a case study. Primary and secondary data were used for the study. The primary data came from a semi-structured interview, and the secondary data were from official databases.

The work highlights two important results. The first allows the definition of a subclassification of indicators by product and organisation, extracting those with a cross-linked characteristic concerning the waste scope. Secondly, the indicators' application shows the farm's circular and waste valorisation potential within the Bio-District. The study also made it possible to test a new indicator, the “Potential Energy Biomass Recovery”, to measure the farm's potential to produce energy from waste.

This research proposes a new circular economy approach to evaluate waste management in the agri-food sector.