1111 papers
This study demonstrates that a machine learning-augmented logistic regression model outperforms traditional Jones-model approaches in detecting earnings manipulation among African listed firms, while highlighting significant performance heterogeneity across countries due to varying institutional contexts.
This descriptive case study of a single Lubbock household's grocery purchases from 2023 to 2026 demonstrates how item-level data can quantify a net realized price burden of $298.96 over 37 months while illustrating how product mix, retailer choices, and potential rebates influence actual household spending.
Analyzing seven rounds of India's Periodic Labour Force Survey (2017–2024), this study finds that platform-mediated gig work represents a continuation of pre-existing informal labour rather than a transformative new phenomenon, characterized by modest non-monotonic growth, a declining education gradient, and a gender gap largely driven by classification differences regarding unpaid family labour.
This paper presents a simulation-based framework using synthetic data to evaluate how threshold-based physician incentive compensation structures create concentrated areas of sensitivity, offering healthcare leaders a new perspective on program design that considers structural risk alongside expected payouts.
This study of Shenzhen's drone logistics subsidies reveals that while extensive operational data is collected, a critical administrative gap exists between flight records and fiscal claims, as current reward mechanisms fail to incorporate exposure factors like population density, noise, or complaints into payment calculations.
This descriptive study demonstrates that luxury hotels can effectively reduce post-consumer food waste through strategic floor operations—such as controlled refilling, live cooking stations, and staff training—without compromising guest perceptions of luxury or service quality.
The paper proposes DART, a Domain-Aware Relational Transformer that combines a Multi-Scale Temporal Fusion module and a Domain-Knowledge-Guided Sparse Attention mechanism to achieve state-of-the-art stock prediction performance and significantly reduced computational costs on large-scale datasets.
This systematic literature review synthesizes fragmented scholarly evidence on renewable energy financing frameworks, analyzing their typologies, effectiveness across varying institutional contexts, and research gaps to provide a structured analytical guide for scholars, policymakers, and investors.
This study of 23 Egyptian listed firms from 2003 to 2023 reveals that debt maturity's impact on corporate growth is heterogeneous and significantly conditioned by financial agility (cash holdings and net working capital), with fixed-effects robustness checks suggesting these relationships are associative rather than strictly causal.
This paper presents the first direct monetary comparison showing that the productivity gains from AI adoption in the workplace, estimated at nearly 9% of U.S. GDP, vastly outweigh the associated energy costs, which remain below 0.1% of GDP.