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Essays on Artificial Intelligence and Firm Performance. The Italian Evidence
No full textL’intelligenza artificiale (AI) sta trasformando i contesti aziendali, generando al tempo stesso opportunità e incertezza. La letteratura offre risultati eterogenei: alcune imprese considerano l’AI una risorsa strategica, mentre altre la percepiscono come costosa e rischiosa. Poiché il vantaggio competitivo è spesso collegato all’imprenditorialità aziendale (corporate entrepreneurship, CE), comprendere come l’adozione dell’AI si intrecci con la CE rappresenta un ambito di ricerca rilevante. Tuttavia, nonostante il crescente interesse per AI e performance aziendale (FP), la relazione tra AI e CE rimane poco indagata.
Questa tesi di dottorato colma tale lacuna attraverso quattro saggi sulle PMI italiane, analizzando i fattori che guidano l’adozione dell’AI e i suoi effetti su CE e FP nelle imprese familiari e non familiari. Il primo saggio offre una revisione quali-quantitativa di 84 articoli Web of Science (1950–2024), individuando sei filoni di ricerca e prospettive future sul tema AI-CE.
Il secondo saggio esamina se il potere di mercato influenzi l’adozione dell’AI. Basandosi su un’indagine nazionale integrata con mark-up aziendali, emerge che le imprese familiari adottano l’AI in modo proattivo sia con feedback di performance positivi sia negativi. In condizioni di rischio elevato, continuano a investire con segnali positivi ma limitano l’adozione con segnali negativi. Le imprese non familiari, invece, riducono gli investimenti in AI durante fasi favorevoli e reagiscono raramente a feedback negativi, suggerendo una percezione dell’AI come risorsa onerosa più che strategica.
Il terzo saggio analizza, tramite PCA, come l’AI influenzi la CE. Emergono quattro componenti: innovazione di prodotto, sostenibilità, efficienza energetica e insight/patenting. L’AI per l’innovazione di prodotto rafforza la CE; la sostenibilità mostra effetti eterogenei; l’efficienza energetica sostiene strategie di prodotto e mercato; l’AI per insight e brevetti non presenta associazioni significative con la CE.
Il quarto saggio indaga i fattori finanziari e gli esiti di performance. Le imprese più giovani e profittevoli adottano più facilmente l’AI. Dopo l’adozione, crescono le attività di CE—nuovi progetti, modifiche dell’offerta, riposizionamenti strategici—insieme a miglioramenti in vendite, occupazione, produttività e valore di mercato.
Nel complesso, la tesi chiarisce come l’adozione dell’AI si intrecci con CE e FP, offrendo nuove evidenze sulla competitività e sulla trasformazione imprenditoriale.Artificial intelligence is reshaping business environments, generating opportunities as well as uncertainty. Research offers mixed views: some firms treat AI as a strategic asset, while others see it as costly and risky. Because competitive advantage is often linked to corporate entrepreneurship (CE), understanding how AI adoption interacts with CE has become an important research area. Yet, despite rising interest in AI and firm performance (FP), its relationship with CE remains underexplored.
This Ph.D. thesis addresses this gap through four papers on Italian SMEs, examining what drives AI adoption and how it affects CE and FP in family and non-family firms. The first paper provides a quali-quantitative review of 84 Web of Science articles (1950–2024), identifying six research streams and future directions in AI-CE research.
The second paper studies whether market power influences AI adoption. Using a national survey matched with firm-level markups, results show that family firms adopt AI proactively under both positive and negative performance feedback. Under high risk, they continue to invest with positive signals but restrict adoption with negative ones. Non-family firms reduce AI investment when performing well and seldom react to negative feedback, suggesting they view AI as resource-heavy rather than strategic.
The third paper examines how AI adoption shapes CE using PCA. Four AI components emerge: product innovation, sustainability, energy efficiency, and consumer insight/patenting. Product-innovation AI strongly enhances CE; sustainability AI has mixed effects; energy-efficiency AI supports product and market strategies. Consumer-insight/patenting AI shows no significant link to CE.
The fourth paper explores financial drivers and performance outcomes. Younger and more profitable firms adopt AI more often. After adoption, firms show stronger CE activity; new projects, product changes, and strategic repositioning, along with improvements in sales, employment, productivity, and market value.
Overall, the thesis clarifies how AI adoption interacts with CE and FP, offering insights into competitiveness and entrepreneurial transformation
Medium-Temperature Heat Pumps for Sustainable Urban Heating: Evidence from a District Network in Italy
No full textThe decarbonisation of urban heating systems represents a key challenge for the transition towards sustainable cities. This study investigates the field integration of a Medium-Temperature Heat Pump (MTHP) within the Osimo District Heating Network (DHN) in Italy, demonstrating how low-grade return flows (30–50 °C) can be effectively upgraded to supply temperatures of 65–75 °C, in line with 4th-generation district heating requirements. Specifically, 5256 h of MTHP operation within the DHN were analysed to validate the initial design assumptions, develop surrogate performance models, and assess the system’s techno-economic and environmental performance. The results indicate stable and reliable operation, with a weighted average Coefficient of Performance (COP) of 3.96 and a weighted average thermal output of 134.5 kW. From an economic perspective, the system achieves a payback period of approximately six years and a Levelised Cost of Heat (LCOH) of 0.0245 €/kWh. Environmentally, the MTHP enables CO2 emission reductions of about 120 t compared with conventional gas-fired boilers. Beyond its technical performance, the study highlights the strong replicability of MTHP solutions for small- and medium-scale DHNs across Europe. The proposed approach offers urban utilities a scalable and cost-competitive pathway towards low-carbon heat supply, directly supporting municipal climate strategies and aligning with key EU policy frameworks, including the European Green Deal, REPowerEU, and the “Fit-for-55” package
Evaluating population health and healthcare access through secondary health data: from chronic conditions to health emergencies
No full textLa crescente disponibilità di informazioni sanitarie raccolte sistematicamente nei database amministrativi elettronici (HUDs) ha trasformato la valutazione della salute della popolazione. Supportati da adeguate metodologie epidemiologiche e statistiche, gli studi basati su HUDs consentono di analizzare efficacia dei trattamenti, andamento delle malattie, stratificazione del rischio e accesso alle cure nella pratica reale, aspetti non completamente rilevabili dagli studi randomizzati controllati, fornendo quindi evidenze a supporto delle decisioni sanitarie. Nonostante lo sviluppo di metodi, algoritmi e disegni di studio volti a superare i limiti dei dati amministrativi, permangono sfide legate alla loro applicazione in contesti complessi e alla disponibilità delle informazioni, che possono limitare la solidità delle evidenze prodotte. La tesi analizza le potenzialità degli HUDs e le principali sfide metodologiche nell’utilizzo di tali dati per generare evidenze real-world sulla salute della popolazione e sull’accesso all’assistenza sanitaria in cinque ambiti specifici.
Nella prima parte della tesi vengono descritte le caratteristiche degli HUDs, con particolare riferimento al contesto italiano, i loro vantaggi e limiti come fonte di dati per la ricerca epidemiologica, e una panoramica dei metodi utili a superarli.
La seconda parte presenta gli studi sviluppati nell’ambito della ricerca di dottorato. Data l’importanza delle misure di multicomorbosità come strumenti di stratificazione dei pazienti per fornire cure personalizzate e per la programmazione sanitaria, è stata effettuata la validazione esterna del Multisource Comorbidity Score in un contesto diverso da quello italiano in cui è stato sviluppato. Lo studio evidenzia il potenziale dello strumento ad essere trasferito in paesi con sistemi sanitari analoghi, sottolineando la necessità di valutare con attenzione le differenze nei sistemi di codifica, nella disponibilità dei dati e nell’organizzazione dell’assistenza sanitaria.
I due ulteriori studi di popolazione hanno indagato la salute e l'assistenza sanitaria nelle malattie croniche, per fornire evidenze sulla coesistenza di diabete e celiachia e sull'incidenza di ricoveri evitabili nei pazienti diabetici correlati al background migratorio. Gli studi affrontano considerazioni per un'appropriata selezione della coorte e la capacità di identificare lo status migratorio attraverso gli HUDs, nonché l'impatto della disponibilità temporale di questi ultimi per studiare eventi rari.
La tesi approfondisce inoltre l’uso degli HUDs nella sorveglianza di condizioni acute, le lesioni cerebrali acquisite e le forme severe. Oltre ai limiti intrinseci, come la mancanza di scale di gravità clinica negli HUDs, lo studio riporta l’utilizzo di più fonti di dati e lo sviluppo di algoritmi per tracciare la condizione di interesse. I risultati dell'analisi del trend forniscono informazioni rilevanti per la pianificazione sanitaria e le strategie di prevenzione.
Infine, l’impatto della pandemia di SARS-CoV-2 sull’accesso al servizio di emergenza è stato analizzato mediante l’analisi delle serie temporali interrotte, evidenziando riduzioni persistenti nell’accesso ai dipartimenti di emergenza durante la pandemia, con pattern diversi rispetto ai livelli di gravità. Sebbene i dati si riferiscano a un singolo dipartimento, le considerazioni metodologiche possono essere facilmente trasferite utilizzando gli HUDs per fornire una valutazione basata sulla popolazione utile per orientare le future strategie sanitarie in condizioni di emergenza.
La tesi evidenzia il ruolo degli HUDs nel supportare la valutazione della salute della popolazione, l'accesso alle cure e la performance dei servizi in contesti cronici, acuti ed emergenziali. Allo stesso tempo, evidenzia sfide persistenti che indicano chiare direzioni per futuri miglioramenti metodologici e infrastrutturali.The growing availability of routinely collected health information stored in electronic Healthcare Utilization Databases (HUDs) has transformed the assessment of population health. When supported by appropriate epidemiological and statistical methodologies, studies based on HUDs allow the analysis of treatment effectiveness, disease trends, risk stratification, and access to care in real-world practice, dimensions that randomized controlled trials cannot fully capture, thus generating evidence to inform healthcare decision-making. Despite the developments in recent years of methods, algorithms, and study designs aimed at addressing the limitations of observational research based on these secondary healthcare data, challenges remain in adapting these methodological approaches to complex real-world contexts and to the actual availability of information, in order to generate credible evidence. To this end, the thesis explores the potential of HUDs and methodological and technical challenges that arise when these data are used to generate real-world evidence on population health and healthcare access across five specific settings.
The thesis is structured as follows. First, characteristics of HUDs, with specific consideration of the Italian context, their advantages and limitations when used as a data source for epidemiological research, and an overview of the methods to overcome these limits are provided.
The second part pertains to the specific studies conducted within the framework of this doctoral research. Given the particular importance of multicomobidity measures as tools for patient stratification to provide personalized healthcare, and for health policy planning, the external validation of the Multisource Comorbidity Score in a context different from the Italian in which it was developed, was performed. The study supports the potential of the tool to be transferred in countries with similar healthcare systems, provided that differences in coding systems, data availability, and healthcare organizational systems are carefully examined.
The two additional population-based studies explored health and healthcare in chronic diseases, to provide evidence on the coexistence of diabetes and coeliac disease and the occurrence of avoidable hospital admissions in diabetic patients related to the immigrant background. The studies address considerations for appropriate cohort selection and the ability to identify immigrant background through HUDs as well as the impact of the temporal availability of the latter to study rare events.
The thesis further explores the use of HUDs in the surveillance of acute conditions, focusing on acquired brain injury and its severe cases. Despite inherent limitations, particularly the lack of clinical severity scales recorded in HUDs, the integration of multiple data sources and the development of algorithms to detect the disease are reported. Results related to the trend analysis provide relevant insights for healthcare planning and prevention strategies.
Finally, the impact of the SARS-CoV-2 pandemic on access to emergency care was examined using interrupted time series analysis, revealing persistent reductions in emergency department utilization during the pandemic, with different patterns according to the urgency level. Although the data refer to a single emergency department, methodological considerations can be easily transferred using HUDs to provide a population-based evaluation useful in guiding future emergency healthcare strategies.
The thesis emphasizes the role of HUDs in supporting population health assessment, access to care, and service performance in chronic, acute, and health emergency contexts. At the same time, it highlights persistent challenges, which indicate clear directions for future methodological and infrastructural improvements
Spontaneous oscillations in eukaryotic cilia and photo-responsive rods
Full text linkWe present a comparative analysis of the chemo-mechanical mechanisms that drive spontaneous oscillations in two distinct active filamentous structures: photo-chemically deformable liquid crystal elastomer (LCE) rods and ATP-powered eukaryotic cilia. Using a unified framework of active planar rods, we develop simplified mathematical models for both systems. We reduce the governing partial differential equations to one-degree-of-freedom (1-DOF) nonlinear oscillators, each undergoing a supercritical Hopf bifurcation. For these reduced models, we obtain explicit analytical expressions for the onset and characteristics of self-sustained oscillations. Despite the common mathematical structure, the underlying physical mechanisms are fundamentally different. For LCEs, self-oscillation is an inertial phenomenon driven by an elastic-inertial feedback over the timescale of the photochemical reaction. In contrast, cilia live in the inertia-less regime, and the instability is driven by a negative effective damping (motive force) that arises from the mechanochemistry of molecular motors. The analytical predictions for critical activation thresholds, frequencies, and amplitudes agree with full nonlinear simulations, providing quantitative insight into the dynamics of these complex self-oscillating systems
Perceptual and physiological virtual assessment of indoor Living Walls including lighting settings
Full text linkLiving Walls (LWs) are increasingly introduced to improve air quality and aesthetics indoors, but the supplementary lighting required for plant vitality can disrupt occupant comfort. Thus, LW design should adopt an integrated approach, combining system requirements definition with users’ comfort. Immersive Virtual Environments can support LWs prototyping, reliably creating and comparing alternative design scenarios. This study proposes a modular LW and employs virtual scenarios as a pre-design strategy to evaluate the influence of different configurations on users’ visual perception and physiological state. The baseline scenario without greening is compared with the simple LW implementation (LW scenario) and with LW integrated by specific horticultural lighting, installed on an overhead supporting bracket, and properly shielded to ensure a proper work plane illuminance (LWL). The system is applied to a virtual university classroom, modelled and validated in respect of its real-world conditions. 41 subjects experienced each virtual scenario in a repeated-measure design study, rating realism, task suitability, visual comfort, and configuration preference, while physiological parameters were also monitored. Statistical analysis revealed a high realism in all scenarios, and similar perceived quality levels for task suitability. LWL, as expected, was perceived as 30 % brighter, 25 % more glare than baseline and LW, but physiological and eye-tracking metrics remained stable across scenarios, confirming no stress-related implications, thanks to proper trade-offs between LW design and dedicated lighting implementation. Presence in virtual scenarios was high and cybersickness negligible, validating the ecological fidelity, supporting the potential of this multi-step, user-centred methodology for optimising biophilic interventions during the pre-design phase
Cooling Strategies to Improve the Built Environment: Experimental Characterization, Model Calibration, and Multi-Climate Analysis of Innovative Ventilated and Air Permeable Roofs
Full text linkUrban Heat Island effects and the general rise in outdoor temperatures are increasing the cooling demand in buildings. As a consequence, electrical cooling systems are becoming more common, increasing energy consumption and thus resulting in negative environmental impacts. Optimizing passive solutions that require no energy input can provide substantial benefits for building energy efficiency and urban sustainability. This study presents a research activity, financed by the EU-funded project LIFE SUPERHERO, that enhances existing roofing technologies based on passive cooling; defines an experimental method to assess their benefits in terms of energy savings; and finally evaluates their effectiveness in future climate scenarios based on greenhouse gas Representative Concentration Pathways across a set of mid-temperate/hot climate locations, also in comparison with traditional unventilated roofs. A new Climate Adaptation Efficiency Index (CAEI) was introduced to evaluate the energy efficiency potential of buildings equipped with highly ventilated and permeable clay tile roofs compared to a baseline scenario without the intervention. The results confirm the potential of ventilated and air-permeable roofs to reduce incoming heat flux and support cooling energy-efficiency planning. Indeed, CAEI values were above 20%, reaching 45–50% in hot Mediterranean and arid climates and 28–33% in cooler/temperate contexts. Under future climate scenarios, benefits further increase in the hottest Mediterranean locations, reaching up to 66%, while rising to about 44% in temperate climates, with an average increase of 10–15 percentage points, highlighting the strong potential of highly ventilated and air-permeable clay tile roofs as an effective, affordable, sustainable, and easy-to-install climate adaptation strategy
Smart roads, smart cities e diritti fondamentali
No full textIl contributo si sofferma sulla crescente interazione tra nuove tecnologie e spazi urbani, ponendo in evidenza l'incidenza della digitalizzazione di edifici e città sulla tutela dei diritti fondamentali della persona umana.The contribution focuses on the growing interaction between new technologies and urban spaces, highlighting the impact of the digitization of buildings and cities on the protection of fundamental human rights
Exploiting inertial sensing for human motion analysis and motor dynamics recognition
No full textLa crescente richiesta di monitoraggi continui, poco invasivi e a distanza del movimento umano sta orientando la biomeccanica verso l’uso di sensori indossabili, in particolare le unità di misura inerziali (IMU/MIMU). Sebbene i sistemi di motion capture da laboratorio restino il riferimento più accurato, costi elevati, complessità e volume di misura limitato ne impediscono l’uso prolungato e in contesti reali. I sensori inerziali rappresentano quindi un’alternativa promettente, pur introducendo sfide metodologiche come l’allineamento sensore–anatomia, la deriva del segnale e l’interpretazione dei dati in vita quotidiana, soprattutto in popolazioni con deficit motori.
In questo contesto, questa tesi approfondisce le basi metodologiche, le strategie computazionali e le applicazioni pratiche dei sistemi basati su MIMU per l’analisi del movimento umano, concentrandosi su tre aree: riconoscimento delle attività (HAR), stima della cinematica degli arti inferiori e valutazione della stabilità dinamica tramite il margine di stabilità (MoS). L’obiettivo è sviluppare soluzioni indossabili accurate, robuste e clinicamente utili per il monitoraggio quotidiano.
Il tema dell’HAR è stato investigato tramite due approcci: uno basato su algoritmi di machine learning “shallow” e uno su modelli di deep learning. Entrambi mostrano che una singola MIMU al polso può riconoscere gesti clinicamente rilevanti come bere o assumere una pillola. Configurazioni sensoriali essenziali hanno comunque prodotto elevata accuratezza. Caratteristiche ben ingegnerizzate hanno permesso ai modelli più semplici di raggiungere prestazioni comparabili a reti più complesse, mentre architetture ibride di deep learning hanno ulteriormente migliorato accuratezza e robustezza usando direttamente i segnali grezzi. È stato inoltre valutato il contributo informativo delle diverse tipologie di segnale.
La tesi analizza anche la stima della cinematica degli arti inferiori per la valutazione del cammino e la riabilitazione. È stato sviluppato un nuovo metodo di identificazione degli assi anatomici, basato su movimenti funzionali e minimizzazione ai minimi quadrati degli assi elicoidali istantanei, per allineare il frame del sensore a quello anatomico e ottenere stime articolari significative e clinicamente coerenti. È stata inoltre valutata la possibilità di ridurre i movimenti richiesti nella fase di calibrazione senza compromettere l’accuratezza nel piano sagittale, analizzando anche le stime nei piani frontale e trasverso. La validazione è stata estesa oltre il cammino lineare, includendo compiti funzionali come la svolta e il Timed Up and Go, confermando l’applicabilità della metodologia a movimenti complessi e reali.
Infine, la stabilità dinamica è stata esaminata tramite la stima del MoS utilizzando solo tre sensori inerziali. Un metodo completamente basato su MIMU per questa misura è ancora poco esplorato, e questo lavoro contribuisce a definirne la fattibilità come tecnica non invasiva per quantificare la stabilità dinamica, un indicatore cruciale del rischio di caduta, soprattutto negli anziani e nei soggetti fragili. Sebbene preliminari, i risultati mostrano un buon accordo tra misure inerziali e marker-based sia in soggetti sani sia in persone con Parkinson, evidenziando il potenziale dell’approccio e il ruolo diagnostico dei compiti di svolta.
Nel complesso, questi contributi gettano le basi per tecnologie indossabili di nuova generazione a supporto della diagnosi precoce, della riabilitazione personalizzata e del monitoraggio a lungo termine della funzione motoria in contesti reali.The increasing demand for continuous, unobtrusive, and remote monitoring of human movement is shifting biomechanics toward wearable sensors, particularly inertial measurement units (IMUs/MIMUs). While laboratory-based motion capture remains the gold standard, its cost, complexity, and limited acquisition volume prevent long-term and real-world use. Wearable inertial sensors offer a valid alternative, enabling prolonged monitoring with minimal intrusion, but they introduce methodological challenges such as sensor–anatomy alignment, signal drift, and context interpretation, especially in populations with motor impairments.
In this context, this thesis investigates the methodological foundations, computational strategies, and practical applications of MIMU-based systems for human motion analysis, focusing on three areas: human activity recognition (HAR), lower-limb joint kinematics, and dynamic stability assessment through the margin of stability (MoS). The aim is to develop accurate, robust, and clinically meaningful wearable solutions for everyday monitoring.
Inertial-based HAR has been addressed through two frameworks: one using shallow machine-learning classifiers and another relying on deep-learning models. Both approaches show that a single wrist-mounted MIMU can reliably recognize clinically relevant upper-limb gestures, such as drinking or pill intake. High classification accuracy was achieved even with minimal sensor configurations. Carefully engineered features enabled machine-learning models to match more complex networks, while hybrid deep-learning architectures further improved accuracy and robustness using raw inertial data. The contribution of individual signals and their combinations was also examined.
The thesis also explores lower-limb kinematic estimation for gait analysis and rehabilitation. A new anatomical axis identification method was developed, using functional movements and least-squares minimization of instantaneous helical axes to align sensor and anatomical frames. This enabled meaningful and clinically compliant joint angle estimation. The study also assessed whether calibration movements could be reduced without compromising sagittal-plane accuracy, while also evaluating performance in the frontal and transverse planes. Validation extended beyond straight-line gait to include clinically relevant tasks such as turning and the Timed Up and Go, demonstrating the method’s applicability to complex, real-world movements.
Dynamic stability was further investigated through MoS estimation using only three inertial sensors. Fully MIMU-based MoS computation is still largely unexplored, and this work contributes to establishing it as a feasible, unobtrusive technique for quantifying dynamic stability—a key predictor of fall risk, especially in older or frail individuals. Although preliminary, the results show good agreement between inertial- and marker-based measures in both healthy and Parkinson’s disease participants, highlighting the potential of this approach and the diagnostic importance of turning tasks.
Overall, these contributions support the development of next-generation wearable technologies for early diagnosis, personalized rehabilitation, and long-term monitoring of motor function in real-world environments
Outcome measures for health interventions targeting multimorbid older adults: A systematic review
Full text linkBackground Older adults with multimorbidity are often excluded from clinical trials. Traditional disease-centered endpoints may be inadequate for this population, presenting unique challenges related to frailty, functional decline and disability. Objectives To systematically review the literature on outcomes considered relevant in interventions targeting older adults with multimorbidity, based on both patient and healthcare professionals’ perspectives. Methods This systematic review followed PRISMA 2020 guidelines and was registered in PROSPERO (CRD42023478249). We searched five electronic databases for primary quantitative and qualitative studies including patients aged ≥ 60 years with ≥ 2 chronic conditions. Outcomes were categorized into six domains: 1) Physical conditions/outcomes; 2) Mental conditions/outcomes; 3) Psychosocial outcomes/general health; 4) Healthcare utilization and costs; 5) Patients’ behaviors; 6) Care process outcomes. We conducted a narrative synthesis and compared outcomes across qualitative and quantitative studies. Results Seventy-one studies were included (53 quantitative, 16 qualitative, 2 mixed-methods). The most frequently reported outcomes fell under Psychosocial outcomes / General health (69.0 %), followed by Care process outcomes (52.1 %) and Healthcare utilization and costs (49.3 %). Qualitative studies more often addressed Mental health outcomes (43.8 %). Maintaining independence, physical function, and quality of life emerged as most important outcomes for older adults. Conclusions In intervention studies involving older adults with multimorbidity, outcomes should move beyond disease-specific measures to include independence, physical function and quality of life. Outcome selection should account for patient clinical heterogeneity, frailty, and life expectancy to ensure relevance and impact in this complex population