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Revolutionizing consumption: Unveiling the Allure of NFTs and digital twins for sustainable luxury fashion
No full textNon-fungible tokens (NFTs) are revolutionizing luxury fashion by offering digital experiences that promise innovation, exclusivity, and sustainability. While luxury brands increasingly experiment with these technologies, little is known about how they influence consumer perceptions of sustainability, brand legitimacy, and purchase likelihood. Drawing on dematerialization theory, institutional and legitimacy theory, and the sufficiency model, this research investigates NFTs’ role in promoting sustainable consumption and brand legitimacy. Building on insights from a preliminary qualitative study, three experiments test how product type (non-NFT, NFT, digital twin) affects purchase likelihood and how perceived product sustainability and brand legitimacy moderate and mediate these effects. Study 1 shows that digital twin products combining physical and NFT components yield the highest likelihood of purchase. Study 2 finds the positive effect of NFTs strengthens when perceived product sustainability is high. Study 3 reveals perceived product sustainability acts as a boundary condition, shaping how product type influences brand legitimacy and purchase likelihood. Findings offer theoretical insights and actionable guidance for manager
Path-Dependence and Persuasion: A Theory-Based View
No full textThis dissertation extends the theory-based view of strategy by looking into two fundamental challenges: how decision-makers' theories and internal beliefs influence path dependent trajectories, and how these theories are implemented when multiple organizational actors must align.
The first extension examines how process memory shapes strategic outcomes. Paper 1 develops a theoretical framework showing that decision-makers' awareness of path dependence and their theories about future contingencies influence the initial conditions that trigger path-dependent processes. Rather than being merely passive victims of "historical accidents," decision-makers can actively shape their likelihood of avoiding suboptimal lock-ins through their theoretical frameworks. Paper 2 provides experimental validation, revealing that path dependence awareness alone does not alter behavior—only when combined with optimism about optimal alternatives does it lead to better long-term choices.
The second extension addresses organizational complexity in the presence of more than one decision maker. Paper 3 develops a framework for how decision-makers achieve alignment around new theories through three distinct persuasion mechanisms: information persuasion (strategic evidence disclosure), interpretative persuasion (reframing existing data), and theory persuasion (fundamental changes to causal models). This persuasion process bridges the critical gap between strategy formulation and implementation when traditional incentive mechanisms prove insufficient.
Together, these papers demonstrate that successful strategy requires managing both temporal dynamics (path-dependent consequences) and social dynamics (organizational alignment). The findings have significant implications for how leaders navigate uncertainty, suggesting that effective strategy requires not just good theories, but also the ability to understand their evolution over time and spread within organizations
"As Long As You Make Money": An Inquiry Into the Criminogenic Effects of the Profit Motive
No full textCorporations are born to make money. Although the corporate purpose is still a highly debated topic, shareholder value maximization is a dominant theory in modern corporate law. Managers – the agents – must steer the corporation to maximize shareholder value in the best interest of the principals – those whose money is at stake. However, the relentless pursuit of profits can push agents to misbehave. Thrilled by juicy stock options, pressured by short-termism, and threatened by the risk of failure, some officials can reach the point where delinquency becomes a justified means for corporate success. Yet, the relationship between the profit motive and misbehavior lacks a solid theoretical framework to make sense of their causal connection and provide actionable policy proposals. Corporate purpose is primarily examined in the context of corporate governance, whereas white-collar criminology is often less concerned with the technicalities of profit maximization in modern firms. A timely analysis, this paper aims at filling a gap in the literature by offering a systematic approach to the complicated relationship between shareholder value maximization and misbehavior, offering incentive-based and cooperation-based countermeasures and proposals to minimize criminogenic risks in value maximizing companies
Sustainability assurance
No full textThe article investigates the role of sustainability assurance provider
Colpi di Stato e diritto costituzionale comparato: effettività, sovranità e nuove forme di discontinuità costituzionale
No full textL'editoriale discute il colpo di stato militare come modalità di accesso al potere così come il ruolo dele forze armate, statali e private contractors, nella gestioen del potere in Africa
New Perspectives on Poverty among Single Parents: Accounting for Diversity and Pre-Parenthood Characteristics
No full textPersistent poverty remains a pressing issue in the United States (U.S.), particularly in comparison to other high-income countries. A dominant explanation for high poverty rates in the U.S. centers on family structure – especially the declines in marriage rates and the rise in mother-headed households. This line of reasoning aligns with behavioral explanations to poverty, which emphasize personal responsibility and individual choices. In contrast, this dissertation adopts an institutional and life-course perspective, arguing that poverty among single parents is not a consequence of family structure per se, but a reflection of long-term disadvantage and insufficient welfare support.
While existing institutional theories have highlighted the importance of welfare regimes in mitigating poverty, they often treat single parenthood as a monolithic or static experience. This thesis argues that poverty must be understood as a cumulative outcome of life-course disadvantages and inadequate policy support. In doing so, it aims to adapt institutional theories to better account for the diversity and lived realities of single parents.
Using longitudinal panel data from five high-income countries (U.S., United Kingdom, Australia, Germany, Switzerland), the thesis analyzes the poverty experiences of single parents over time. It accounts for heterogeneity in single parenthood by distinguishing pathways into single parenthood (divorce, separation, widowhood, nonmarital births) and by exploring the sociodemographic composition of these pathways across time. Then, it explores different poverty outcomes of single parents and their children across these pathways and across time. Using decomposition techniques, it disentangles the relative contribution of family structure, pre-parenthood disadvantage, and policy interventions to poverty outcomes.
The results show that poverty among single parents is not evenly distributed. Never-married and separated single parents in the U.S. face the highest poverty risks, while divorced and widowed parents fare better. Cross-national comparisons reveal stark differences: U.S. single parents are uniquely vulnerable, in part due to weak income protection policies. Importantly, the pathways into single parenthood explain within-country variation but do little to explain cross-national differences—policy generosity is the key factor in explaining cross-country differences. Longitudinal analysis reveals that many single parents already experience poverty up to a decade before becoming parents. Over 60% of single-parent poverty is attributable to pre-parenthood conditions, suggesting that single parenthood mediates, rather than causes, adult poverty. These findings challenge the logic of marriage promotion as an anti-poverty strategy and instead point to the need for comprehensive welfare programs to reduce child poverty.
This dissertation reframes the debate on the relationship between single parenthood and poverty. It challenges behaviorally driven explanations and demonstrates that poverty among single-parent families stems not from marital choices but from life-long disadvantages and policy failure. By integrating insights from life-course analysis, and within-group differentiation, it extends the theoretical scope of institutional explanations to poverty. The findings underscore the importance of comprehensive policies to improve material resources among single-parent families as opposed to marriage promotion
Navigating Rough Landscapes: Statistical Physics Approaches to Machine Learning and Inference
No full textArtificial Intelligence (AI) has rapidly become a central field in contemporary research, driving innovation in several disciplines, including physics, engineering, economics, and medicine. At the heart of AI lies the development of algorithms capable of autonomously learning from data and adapting to new challenges, moving beyond traditional rule-based programming. Despite remarkable progress, the underlying reason for the success of many AI models remains largely unexplained, underscoring the need for a deeper theoretical understanding of their inner mechanisms.
This thesis addresses this challenge by employing tools from statistical physics to analyze and interpret machine learning algorithms, with a particular focus on the geometry of their loss landscapes. By leveraging methods such as the replica method, random matrix theory, and message passing algorithms, the thesis investigates how the structure of the loss landscape influences inference, sampling, and optimization in high-dimensional settings.
The thesis investigates four canonical machine-learning tasks, each characterized by a non-convex loss landscape: for the negative and binary perceptron we introduce and analyse a stochastic-localization algorithm that fairly samples their solution space; for phase retrieval we recast the task within a phase-selection framework, dividing its combinatorial and continuous components; we develop a theoretical framework for the spectral analysis of neural-network loss Hessians and specialise it to the Tree Committee Machine; and for Restricted Boltzmann Machines we propose pseudo-likelihood maximisation (which can be reframed in term of loss minimization) as a stable and efficient alternative to contrastive divergence based learning.
Building on these concepts, the thesis offers new theoretical insights into the interplay between optimization, inference, and the underlying geometry of complex machine learning models
The Law through the King. U.S. (Procedural) Judicial Activism from a European perspective
No full textThis essay investigates the phenomenon of judicial activism in the United States, with a particular focus on procedural dimensions, through a comparative lens informed by the civil law tradition—especially the Italian legal syste
Bayesian hierarchical modeling of array-structured demographic data
No full textReliable inference for complex demographic phenomena is essential for understanding population health dynamics and informing public policies. The relevance of this endeavor has stimulated increasing interest in rigorous statistical models for high-dimensional demographic data with complex dependence structures. Recent research has provided valuable insights and effective predictive strategies, but often focuses on specific dimensions at the expense of others. This perspective rules out the possibility to infer more nuanced, yet fundamental, demographic patterns that span across multiple dimensions (e.g., calendar years, age classes, causes of death, countries). We contribute to this line of research by developing novel hierarchical Bayesian procedures for joint modeling of cross-sectional and temporal interactions in array-structured demographic data. This thesis addresses, in particular, three main objectives through state-of-the-art methodologies accounting for demographic processes' core characteristics, while incorporating dynamic partitioning mechanisms.
First, we propose a flexible model for age-period log-mortality rates inducing local clusters of countries. To address the functional nature of the age component, we employ b-spline expansions with dynamic coefficients. Local clustering of the log-mortality rates is achieved through a dependent random partition model on the coefficients that allows grouping structures to vary flexibly across different combinations of ages and periods. We unveil unexplored relationships between countries, opening new directions for demographic research.
Second, we extend stochastic block models to analyze sequences of directed networks encoding co-occurrences of underlying and contributing causes of death. We handle categorically weighted edges assuming block-specific Categorical-Dirichlet distributions, implement a double partition framework to account for asymmetric relationships between underlying and contributing causes, and describe the node clusters through dependent random partitions to ensure smooth evolution of block structures across age classes. Application to 2019 US data reveals cause partitioning that moves beyond traditional medical classifications into more nuanced groupings.
Third, we develop a methodology for dynamic clustering of countries based on their age-specific leading cause of death sequences over time. We model each country's sequence as a categorical trajectory and handle their grouping through a mixture model with exponential-distance components based on Hamming distances, which enables characterization of clusters through a modal sequence and scale parameters describing the heterogeneity of each age-group. The induced partition is allowed to evolve smoothly across years through a temporal random partition model, enabling the identification of clustering structures in leading mortality causes which renovate standard epidemiological transition theories
Development of a Performance Measurement Framework for European Health Technology Assessment: Stakeholder-Centric Key Performance Indicators Identified in a Delphi Approach by the European Access Academy
No full textBackground: The objective of this work was to support the implementation of the European Health Technology Assessment Regulation (EU HTAR) and optimize performance of the evolving EU HTA system. Therefore, an inclusive multi-stakeholder framework of key performance indicators (KPI) for success measurement was developed. Methods: A modified Delphi-procedure was applied as follows: (1) development of a generic KPI pool at the Fall Convention 2024 of the European Access Academy (EAA); (2) review of initial pool and identification of additional KPIs; (3) development of prioritized KPIs covering patient, clinician, Health Technology Developer (HTD), and System/Member State (MS) perspectives, and (4) consolidation of the stakeholder-centric KPIs after EAA's Spring Convention 2025. Results: Steps 1 and 2 of the Delphi procedure revealed 14 generic KPI domains. Steps 3 and 4 resulted in four prioritized KPIs for patients (patient input; utilization of patient-centric outcome measures; time to access; equity); six for clinicians (population/intervention/comparator/outcomes (PICO); addressing uncertainty; clinician involvement; transparency; equity and time to access); four for HTDs (PICO; joint scientific consultation (JSC) process; joint clinical assessment (JCA) process; time to national decision making); five from a system/MS perspective (PICO; learning and training the health system; reducing duplication; equity and time to access). The scope of, e.g., the PICO-related KPI, differed between stakeholder groups. Also, several KPIs intentionally reached beyond the remit of EU HTA as they are also dependent on MS-specific factors including national health systems and budgets. Discussion and conclusions: The KPI framework developed here presents a step towards the generation of systematic multi-stakeholder evidence to support a successful implementation of the EU HTAR. The relevance of the identified stakeholder-centric KPIs is confirmed by their alignment with the Health System Goals suggested in the context of "Performance measurement for health improvement" by the World Health Organisation. Implementation of the framework, i.e., measurement of KPIs, is envisioned to provide evidence to inform the 2028 revision of the EU HTAR