Federico Bartolomucci
Phd Student
Federico Bartolomucci is research associate and PhD candidate in Management Engineer at Politecnico di Milano and part of the TIRESIA Research Centre. His fields of expertise are the ones of social impact measurement, technology transfer towards the social economy and tech for good. His research is currently focusing on the governance of cross-sectoral partnerships finalized to use data to generate a positive impact. He is lecturing in multiple courses among with the course on Social Entrepreneurship and the Sustainable and Social Innovation Lab in the MSc degree on Management Engineering. He has recently spent a visiting research period at the Leventhal Center For Advance Urbanisms at the Massachusetts Institute of Technology. His recent research “FintechforGood: unveiling social value creation in the fintech sector” has been awarded by the European Microcredit Network with the 2022 Best Research Award. He is member of the Special Group of the European Commission for the creation of a CoC for the use of data in the Social Economy.
Research topics:
- Data for Good
- Tech for Good
- Social Entrepreneurship
Publications
- Social and Technological Innovation: Cross-Fertilization Needed
In book: Improving Technology Through Ethics
Federico Bartolomucci, Giorgia Trasciani, Francesco Gerli
A growing interest in the use of technology to solve societal problems has raised in recent years. However, technological applications that aim to tackle societal challenges frequently risk having unintended negative consequences, particularly for underrepresented and disadvantaged groups. This is the case, for instance, with data-intensive technologies and applications for the sharing economy. A way to prevent the generation of negative externalities may be the contamination of conventional tech innovation processes with social innovation distilled features. This contamination act could be advantageous for both social and technological actors and strengthen the collective capacity to generate impact through technology. However, it requires rethinking innovation processes and the roles various actors play inside them as well as reconceptualizing innovation drivers and value redistribution mechanisms. In this chapter, we will discuss the potential benefits of contaminating the two paradigms. We conclude with a reflection on the role that Universities play in this scenario.
- Determinants for university students’ location data sharing with public institutions during COVID-19: The Italian case
In: Data&Policy Journal
Valeria Urbano, Federico Bartolomucci, Giovanni Azzone
Data on real-time individuals’ location may provide significant opportunities for managing emergency situations. For example, in the case of outbreaks, besides informing on the proximity of people, hence supporting contact tracing activities, location data can be used to understand spatial heterogeneity in virus transmission. However, individuals’ low consent to share their data, proved by the low penetration rate of contact tracing apps in several countries during the coronavirus disease-2019 (COVID-19) pandemic, re-opened the scientific and practitioners’ discussion on factors and conditions triggering citizens to share their positioning data. Following the Antecedents → Privacy Concerns → Outcomes (APCO) model, and based on Privacy Calculus and Reasoned Action Theories, the study investigates factors that cause university students to share their location data with public institutions during outbreaks. To this end, an explanatory survey was conducted in Italy during the second wave of COVID-19, collecting 245 questionnaire responses. Structural equations modeling was used to contemporary investigate the role of trust, perceived benefit, and perceived risk as determinants of the intention to share location data during outbreaks. Results show that respondents’ trust in public institutions, the perceived benefits, and the perceived risk are significant predictor of the intention to disclose personal tracking data with public institutions. Results indicate that the latter two factors impact university students’ willingness to share data more than trust, prompting public institutions to rethink how they launch and manage the adoption process for these technological applications.
- Fostering Data Collaboratives’ systematisation through models’ definition and research priorities setting
Conference: dg.o 2022: The 23rd Annual International Conference on Digital Government Research
Federico Bartolomucci, Gianluca Bresolin
Data collaboratives have received increasing attention from institutions, practitioners, and academics in recent years. However, the proliferation of initiatives on the topic has created confusion on which types of initiatives fall within its boundaries and the operative models they follow. A condition that, almost a decade after the introduction of the data collaboratives concept, still limits the research field development. To bridge this gap and enhance further research on the topic, the paper first advances, based on literature, a conceptual refinement of the concept, allowing for the distinction between data collaboratives and other forms of initiatives based on open data sharing. Secondly, through the analysis of a dataset of 135 initiatives through categorical variable clustering techniques, the paper proposes an enhanced categorization distinguishing five data collaboratives’ clusters. Each cluster is described focusing on its peculiarities and development challenges. The larger sample size, the holistic approach adopted, and the field maturity allowed us to gain additional insights with respect to previous research on the topic. Results highlight the heterogeneity of initiatives falling inside the concept of data collaboratives and the necessity to address their development challenges by either concentrating on a specific cluster or generating comparative and horizontal studies. From a practitioner’s standpoint, findings enable comparability and enhance the identification of benchmarks, which is an important resource for the sector’s
- FinTech for Good: unveiling social value creation in the fintech sector
EMN Research Award 2022
Federico Bartolomucci, Andrea Petrolati, Veronica Chiodo
Even though many practitioners allude to the concept of FinTech for Good, little attention has been dedicated by academic research to this specific category of social value-oriented FinTech. The study explores the untapped potential of FinTech for Good to generate social value in the financial sector. The study investigates how technological innovations can generate impacts when put at the service of marginalized groups in different geographical and economical contexts and which is the role played by the technology in the value creation process. Design/methodology/approach – We built, for the first time, a database of FinTech for Good organizations through a multi-step sample construction exercise. Firstly, we identified a general population of 13,330 FinTech organizations through three main data sources (Fintastico, Crunchbase, screening of the portfolio of investing funds active in the field). We then proceeded to identify FinTech for Good organizations by applying deductive content analysis (Elo and Kyngas, 2008) to each FinTech actor identified in the previous stage of our sample construction. To do so we adopted the definitions of social impact (Brest and Born, 2013; Vanclay, 2003) and impact entrepreneurship (Billis, 2010; Evers, 2005; Liu and Kio, 2012) as filtering lenses to code mission and activities of each FinTech and identify those impact-seeking organizations aiming to optimise their financial returns within a social impact mission (i.e. FinTech for Good) (Freireich and Fulton, 2009). Our analyses resulted in a final sample of 292 FinTech for Good organizations. To identify and investigate the FinTech for Good social value creation mechanisms, organizations in the dataset have been clustered according to five variables (impact dimension, business function, market reference, innovation technology, geolocation, type of beneficiaries). Key Results – The analysis allowed to outline six FintechforGood archetypes based on what social impact they create, the geographies and beneficiaries in which they operate and the characteristics of their business model. The study identified several tech-enable social value creation processes, in which the intrinsic value produced by technology by overcoming the financial market’s failures (information asymmetry, transaction costs and risk exposure) coupled with the intentionality to address a social issue, allows to generate social value (e.g. financial inclusion, financial literacy, access to investments). Value – The research represents a first empirical effort to frame the concept of FintechforGood, offers a sound interpretation of this emerging concept and in-depth examination of its characteristics thanks to the analysis of an extensive dataset. The paper represents a novel contribution in the fields of finance, TechforGood and social entrepreneurship and enriches the research on Fintech by focusing on their potential to generate social value alongside financial ones. It sheds light on the use of technology as a leverage to generate social value in the financial sector and identifies the impact generated by these enterprises and the mechanism activating the social value creation. The research paves the path for a new research stream at the crossroad between the fields of TechforGood and Social Impact finance.
