Felipe Merlano

Artificial intelligence (AI) relies on large-scale human-produced data, yet the relationships between content creators and AI developers remain poorly understood. This study examines how these relationships are increasingly structured within AI data supply chains. We argue that emerging asymmetries in economic value distribution within these exchanges may reflect a broader pattern of exploitation by design, whereby AI developers initially position themselves as benevolent actors to facilitate large-scale data acquisition before gradually shifting toward more enclosed and extractive models. Drawing on the well-established fairness–trust connection in supply chain management literature, we conceptualise content creator–AI developer relationships as quasi buyer–supplier exchanges characterised by limited provenance, attribution, and compensation. Using an exploratory qualitative design based on interviews with content creators, AI developers, and industry experts, we develop insights into how trust may be cultivated during the early stages of exchange and subsequently undermined as value is extracted.

This paper aims to support decision-making in lean management and increase the interpretability of Artificial Intelligence (AI)-generated recommendations in business operations. Existing explainable AI approaches provide useful guidance on which features should change, but they pay limited attention to the operational cost of implementing those changes and therefore do not adequately address the lean management trade-off between customer value enhancement and resource use. To address this problem, the study integrates sentiment analysis, topic modelling, and a Free Disposal Hull-based counterfactual model within a nonparametric production framework, in which customer satisfaction is treated as the service outcome and operational expenditures are treated as inputs. The framework shows that lean management in the service industry can be considered through the counterfactual analysis of AI-generated outcomes while considering three types of managerially relevant recommendations: the identification of critical service attributes for improvement, the specification of feasible adjustment targets, and the estimation of the minimum cost required to attain the desired performance level. Using the hotel service setting as an illustrative case, the study contributes a lean-oriented and managerially interpretable approach for linking explainable AI with operational decision making under cost-conscious service improvement.

This report presents an external evaluation of HMRC’s (His Majesty's Revenue and Customs) technical demonstrator for the Electronic Trade Documents Act (ETDA). Delivered in collaboration with the Cabinet Office and the International Chamber of Commerce UK’s Centre for Digital Trade and Innovation (C4DTI), the pilot assessed the operational and technical feasibility of digital trade documents for UK customs administration. The ETDA, enacted in September 2023, provides legal equivalence between electronic and paper trade documents. The pilot explored the benefits of digital documentation for both government and business, with a focus on improving customs compliance, reducing administrative costs, and enhancing trade efficiency. The evaluation applied a mixed-method approach. A Theory of Change (ToC) was employed to map intended pilot outcomes and pathways of impact. A meta-analysis of academic and institutional studies benchmarked potential economic effects. A quantitative case study used operational data from a participating customs service provider. Overall, the evidence traces a pathway from micro-level operational efficiencies at the firm level, such as reduced document processing times and lower per-declaration costs, through meso-level reductions in compliance burdens and transaction costs that benefit businesses, to macroeconomic effects on trade volumes, GDP, wages, and producer prices.

Data processing in machine learning and Artificial Intelligence (AI) can be viewed as a conventional supply chain in which data are the primary flowing artefact: they are sourced, generated, collected, stored, and shared across upstream and downstream actors to produce valuable information products and business outcomes. Within AI-data supply chains, we map common training optimisation techniques to explicit extensions of conventional supply chain production technology, focusing on two classes of structural decisions: (i) merger (e.g., ensemble learning, distributed training, and knowledge distillation) and (ii) reconfiguration (e.g., algorithm hardware matching). Although such horizontal mergers and reconfigurations provide a mechanism for productivity growth and improved resilience under disruptions, their implications for overall supply chain productivity remain difficult to forecast. To address this gap, we develop a general framework for evaluating merger and reconfiguration decisions in complex supply chains, with an application to AI-data supply chains. Leveraging key features of the Free Disposal Hull (FDH) model and the free-arrangement assumption in network nonparametric production technologies, we propose a non-radial directional distance function model to support decision-making in merger and reconfiguration-oriented supply chain design. We demonstrate the applicability of the proposed approach using a dataset that represents an AI-data supply chain.

Global labour markets face significant disruption from the rapid advance of artificial intelligence (AI) and automation. Digital or gig economy workers, like freelancers and online independent contractors, are more exposed to the disruptive impacts of technological changes due to their flexible working conditions, which often come with flexible contracts, less robust legal agreements and other unstable working conditions. This study explores how gig economy workers benefit from alternative privacy-enhancing decentralised reputation systems and technologies that enable them to manage information like education, certifications, credentials, and professional experience, both by collecting and sharing information with employers. We propose a blockchain framework comprising three components: (1) Self-sovereign identity (SSI) enabling cryptographically secured, portable control over credentials via decentralised storage; (2) Immutable reputation registries leveraging consensus mechanisms to secure tamper-proof work histories; and (3) Privacy-preserving signalling using zero-knowledge proofs (ZKPs) to let workers selectively disclose reputation metrics without revealing sensitive details. We combine Signalling Theory (ST) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to empirically assess real workers' intentions to use this type of decentralised reputation system. Our framework enhances transparency, worker autonomy, and privacy in the digital economy.

In the conventional regression-discontinuity (RD) design, the probability that units receive a treatment changes discontinuously as a function of one covariate exceeding a threshold or cutoff point. This paper studies an extended RD design where assignment rules simultaneously involve two or more continuous covariates. We show that assignment rules with more than one variable allow the estimation of a more comprehensive set of treatment effects, relaxing in a research-driven style the local and sometimes limiting nature of univariate RD designs. We then propose a flexible nonparametric approach to estimate the multidimensional discontinuity by univariate local linear regression and compare its performance to existing methods. We present an empirical application to a large-scale and countrywide financial aid program for low-income students in Colombia. The program uses a merit-based (academic achievement) and need-based (wealth index) assignment rule to select students for the program. We show that our estimation strategy fully exploits the multidimensional assignment rule and reveals heterogeneous effects along the treatment boundaries.

Moving from the linear to the circular economy is, in theory, simple. In our current model, the linear economy takes, uses, and disposes. In a circular economy, the cycle is to use, reuse, maintain, refurbish, recycle, and compost to avoid waste. Circular business models (CBMs) are at the forefront of the shift towards a more sustainable and circular economy. Adopting circular practices may involve changing a product design to use locally sourced materials, reducing the number of assembly steps, or switching to renewable resources — with the aim of lowering production costs, reducing waste, and sparking innovation. Moving toward a CBM may seem like a lot of effort for little payoff, particularly to small and midsize businesses, but it offers a way to increase sustainability and business opportunity.

Purpose: The expansion of online shopping aligned with challenging economic conditions have contributed to increasing fraudulent retail product returns. Retailers employ numerous interventions typically determined by embedded perspectives within the company (supply side) rather than consumer-based assessments of their effectiveness (demand side). This study aims to understand how customers evaluate counter-fraud measures on opportunistic returns fraud in the UK. Based on the Fraud Triangle and the Theory of Planned Behaviour, we develop an empirically informed framework to assist retail practice. Design/methodology/approach: We collected 485 valid survey responses about consumer attitudes regarding which interventions are effective against different types of returns fraud. First, a principal component section evaluates the policies' effectiveness to identify any policy grouping that could help prioritise specific sets of policies. Second, cluster analysis follows a two-stage approach, where cluster size is determined, and then survey respondents are partitioned into subgroups based on how similar their beliefs are regarding the effectiveness of anti-fraud policies. Findings: We identify policies relating to perceived effectiveness of interventions and create customer profiles to assist retailers in conceptualising potential opportunistic fraudsters. Our product returns fraud framework adopts a consumer perspective to capture the perceived behavioural control of potential fraudsters. Results suggest effectiveness of different types of interventions vary between different types of consumers, which leads to the development of managerial implications to combat the fraud. Originality/value: This study is unique in assessing the perceived effectiveness of a range of interventions based on data collection and advanced analytics to combat fraudulent product returns in omnichannel retail.

The transition to more sustainable and circular business models (CMBs) is complex, and small and medium-sized enterprises (SMEs) often struggle with the intricacies and additional costs of achieving sustainability. To address these challenges, we developed an interactive web application – the Circularity Radar – in the open-source language for statistical computing and data visualisation R. We used the Shiny package, which provides desirable features for businesses such as a choice between local or online deployment that preserves data privacy, an interactive interface that allows users to specify the conditions under which computations are executed, and dynamic visualisations rendered through input variables. The Circularity Radar can provide low-cost insights to guide SMEs and practitioners in retail and manufacturing industries in exploring the drivers of their sustainability transition, assessing their situations, and understanding the obstacles they must overcome. Our tool incorporates a digital companion framework that involves seven elements of circularity: sustainable materials, sustainable operations, eco-design, product stewardship, R-terms, strategic organisational positioning, and social aspects. Additionally, the Circularity Radar presents alternative instruments and metrics businesses can use to quantitatively and qualitatively assess their progress regarding CBM implementation.