Data collaboratives (DCs) represent an emerging form of cross-sectoral partnerships (CSP), enabling participants from various sectors, to share their data with the aim of contributing to solve grand societal challenges. Despite growing interest from practitioners and academics in recent years, these partnerships seldom advance beyond the pilot stage (UN World Data Forum), leaving their potential unexplored (World Economic Forum, 2021). DCs’ development is hindered by various technological and organisational challenges (Susha 2019). Among these, the governance structure is considered a critical determinant of the partnership’s ability to generate systemic impacts (Ruijer, 2022; Ansell and Gash 2008; Emerson et al 2012). Indeed, the right governance structure favours a better collaborative experience, reduces the inefficiencies of collaborative environments, increases their stability, and consequently increases the collaboration’s capacity to reach its objectives. To account for the unique collaboration dynamics sparked by these data-centred and purpose-driven collaborations, the recent literature (Klievink et al., 2018; Ruijer 2021; Susha and Gil-Garcia 2019) focused on understanding the governance dimensions to consider when designing the governance of these collaborative settings. However, the link between different governance configurations and the long-term stability of DCs is still underexplored, and practitioners lack actionable governance models to be applied in practice. Considering the long-term stability of these partnerships a good approximation of their capacity to generate systemic impact, my research seeks to address these knowledge gaps and investigate the governance models of DCs to identify governance design configurations that ensure the long-term stability of the collaboratives. To accomplish this goal, my research has been divided into three phases, with every phase formalized in one of the three papers that comprise the collection of papers. The initial phase involved proposing an enhanced conceptualization of DCs and based on that, conducting a descriptive analysis of a comprehensive dataset comprising DCs operating all over the world. The analysis was based on the application of a hierarchical clustering technique for categorical variables. The study reveals the wide range of projects encompassed by the DC framework, providing insight into the distinct developmental challenges that each face. The results indicate that although some collaborations seek stability and may derive advantages from it, others are inherently transient and therefore fell beyond the scope of this study. After identifying an appropriate empirical context, the research progressed through the investigation of governance dimensions’ design settings favouring DCs’ long-term stability. This research phase is based on the development of sixteen case studies and the comparison between stable and unstable projects. The results present evidence regarding the design configurations of seven distinct governance dimensions that foster collaborative stability. Findings sustain the context-dependency and dynamic nature of DCs’ governance settings. The third phase of the research investigates the design process that leads DCs to adopt a certain governance configuration. It does so to comprehend the interconnections among governance antecedents, inputs, outputs, and outcomes and advance a process proposal for the development of governance schemas for context based DCs. The research employed a process approach utilised to analyse multiple case studies and tested through a participatory action research effort developed in the city of Turin. The research has substantial implications for both theory and practice. First it advances the academic debate surrounding DC governance models (Klievink et al., 2018; Ruijer 2021; Susha and Gil-Garcia 2019) by providing insights into effective governance arrangements that promote the stability of DCs. Third, the research evolves our understanding of governance models, providing evidence on their context-specificity and dynamic nature, thereby establishing a framework that outlines the interconnections between various governance aspects. On a more general level, the research sheds light on the governance of tech-intensive cross-sectoral collaborations and contributes to the theoretical debate on how to approach these types of partnerships. The study strengthens the argument for approaching them as intricate sociotechnical systems (Micheli et al. 2023) and investigating their characteristics from an integrated technological and organisational standpoint. Empirically, the research furnishes practitioners with substantial empirical evidence against which they can benchmark their experience and clear reference frameworks that can serve as a guide for the design and improvement of their initiatives’ governance arrangements.
I data collaboratives (DCs) rappresentano una forma emergente di partenariati cross settoriali, che permettono di condividere dati tra pubblico, privato e terzo settore, con l'obiettivo di generare impatti positivi e così contribuire alla risoluzione di grandi sfide sociali e ambientali. Nonostante l'interesse crescente da parte di professionisti e accademici, queste collaborazioni superano raramente la fase di sperimentazione iniziale, elemento che lascia altamente inesplorato il loro potenziale di generazione di impatto (World Economic Forum, 2021). Lo sviluppo dei DCs è ostacolato da varie sfide tecnologiche e organizzative (Susha 2019). Tra queste ultime, la struttura di governance è considerata un elemento strumentale alla loro capacità di generare impatti sistemici (Ruijer, 2022; Ansell e Gash 2008; Emerson et al 2012). Infatti, una struttura di governance adeguata favorisce un'esperienza collaborativa migliore, riduce le inefficienze degli ambienti collaborativi, aumenta la loro stabilità e, di conseguenza, aumenta la capacità della partnership di raggiungere i propri obiettivi. In tal senso, la letteratura accademica (Klievink et al., 2018; Ruijer 2021; Susha e Gil-Garcia 2019) si è interrogata su quali assetti di governance influenzino maggiormente la gestione dei DCs, tuttavia, il legame tra diverse configurazioni di governance e la stabilità di lungo periodo dei DCs è ancora poco esplorato. La mia ricerca esplora quindi alcune dimensioni di governance, con l’obiettivo di isolare quelle configurazioni che favoriscono la stabilità di lungo periodo dei DCs. La ricerca è divisa in tre fasi. La fase iniziale si basa sull'analisi descrittiva di un ampio campione di DCs operativi in tutto il mondo. L'analisi, basata sull'applicazione di un metodo di hierarchical clustering per variabili categoriche, ha portato all’identificazione di cinque cluster di progetti. I risultati indicano che, sebbene alcune collaborazioni possano trarre vantaggio da una stabilità di lungo periodo, altre sono intrinsecamente transitorie. Queste rispondono quindi a sfide di sviluppo differenti e vanno analizzate in maniera distinta. Il nuovo contesto empirico così definito ha consentito di proseguire la ricerca con l’obiettivo di isolare delle configurazioni di governance che favoriscono la stabilità dei DCs. La seconda fase di ricerca si è basata quindi sullo sviluppo di sedici casi studio e sul confronto tra progetti in grado di raggiungere o meno tale stabilità. I risultati, focalizzati su sette dimensioni di governance, sostengono la natura dinamica dei modelli di governance e la loro dipendenza dal contesto. In continuità con la seconda, la terza fase di ricerca si è invece basata sull’indagine, sviluppata attraverso sedici casi di studio, del processo di design che porta i DCs ad adottare una certa configurazione di governance. La ricerca ha impiegato un approccio process-based per l’analisi dei casi di studio e testato i risultati preliminari attraverso uno sforzo di action-research partecipativa sviluppato nella città di Torino. I risultati forniscono una mappatura processuale delle relazioni tra diverse dimensioni di governance e equipaggiano chi lavora in tale ambito con un processo da seguire durante il design dell’assetto di governance delle proprie iniziative. La ricerca ha implicazioni per la teoria e per la pratica. In primo luogo, la ricerca avanza il dibattito accademico sui modelli di governance dei DCs (Klievink et al., 2018; Ruijer 2021; Susha e Gil-Garcia 2019) fornendo elementi utili al design di modelli stabili nel tempo. Inoltre, la ricerca evolve la nostra comprensione dei modelli di governance delle partnership cross-settoriali che integrano la tecnologia come elemento strumentale alla creazione di valore, riconcettualizzandone i modelli di governance come costrutti dinamici e dipendenti dal contesto. Lo studio rinforza quindi l’opinione di coloro che sostengono la necessità di trattare i DCs come sistemi sociotecnici complessi (Micheli et al. 2023) e sostiene la necessità di analizzare tali sistemi con una visione integrata che comprenda aspetti di governance organizzativa e tecnologica. A livello empirico la ricerca fornisce modelli che potranno servire da guida per il design e il miglioramento di nuove iniziative o favorire il miglioramento dei modelli di governance di iniziative preesistenti.
Fostering systemic change generation through the collaborative use of data : a governance perspective
BARTOLOMUCCI, FEDERICO
2023/2024
Abstract
Data collaboratives (DCs) represent an emerging form of cross-sectoral partnerships (CSP), enabling participants from various sectors, to share their data with the aim of contributing to solve grand societal challenges. Despite growing interest from practitioners and academics in recent years, these partnerships seldom advance beyond the pilot stage (UN World Data Forum), leaving their potential unexplored (World Economic Forum, 2021). DCs’ development is hindered by various technological and organisational challenges (Susha 2019). Among these, the governance structure is considered a critical determinant of the partnership’s ability to generate systemic impacts (Ruijer, 2022; Ansell and Gash 2008; Emerson et al 2012). Indeed, the right governance structure favours a better collaborative experience, reduces the inefficiencies of collaborative environments, increases their stability, and consequently increases the collaboration’s capacity to reach its objectives. To account for the unique collaboration dynamics sparked by these data-centred and purpose-driven collaborations, the recent literature (Klievink et al., 2018; Ruijer 2021; Susha and Gil-Garcia 2019) focused on understanding the governance dimensions to consider when designing the governance of these collaborative settings. However, the link between different governance configurations and the long-term stability of DCs is still underexplored, and practitioners lack actionable governance models to be applied in practice. Considering the long-term stability of these partnerships a good approximation of their capacity to generate systemic impact, my research seeks to address these knowledge gaps and investigate the governance models of DCs to identify governance design configurations that ensure the long-term stability of the collaboratives. To accomplish this goal, my research has been divided into three phases, with every phase formalized in one of the three papers that comprise the collection of papers. The initial phase involved proposing an enhanced conceptualization of DCs and based on that, conducting a descriptive analysis of a comprehensive dataset comprising DCs operating all over the world. The analysis was based on the application of a hierarchical clustering technique for categorical variables. The study reveals the wide range of projects encompassed by the DC framework, providing insight into the distinct developmental challenges that each face. The results indicate that although some collaborations seek stability and may derive advantages from it, others are inherently transient and therefore fell beyond the scope of this study. After identifying an appropriate empirical context, the research progressed through the investigation of governance dimensions’ design settings favouring DCs’ long-term stability. This research phase is based on the development of sixteen case studies and the comparison between stable and unstable projects. The results present evidence regarding the design configurations of seven distinct governance dimensions that foster collaborative stability. Findings sustain the context-dependency and dynamic nature of DCs’ governance settings. The third phase of the research investigates the design process that leads DCs to adopt a certain governance configuration. It does so to comprehend the interconnections among governance antecedents, inputs, outputs, and outcomes and advance a process proposal for the development of governance schemas for context based DCs. The research employed a process approach utilised to analyse multiple case studies and tested through a participatory action research effort developed in the city of Turin. The research has substantial implications for both theory and practice. First it advances the academic debate surrounding DC governance models (Klievink et al., 2018; Ruijer 2021; Susha and Gil-Garcia 2019) by providing insights into effective governance arrangements that promote the stability of DCs. Third, the research evolves our understanding of governance models, providing evidence on their context-specificity and dynamic nature, thereby establishing a framework that outlines the interconnections between various governance aspects. On a more general level, the research sheds light on the governance of tech-intensive cross-sectoral collaborations and contributes to the theoretical debate on how to approach these types of partnerships. The study strengthens the argument for approaching them as intricate sociotechnical systems (Micheli et al. 2023) and investigating their characteristics from an integrated technological and organisational standpoint. Empirically, the research furnishes practitioners with substantial empirical evidence against which they can benchmark their experience and clear reference frameworks that can serve as a guide for the design and improvement of their initiatives’ governance arrangements.| File | Dimensione | Formato | |
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https://hdl.handle.net/10589/221955