This weekly digest tracks what is NEW or CHANGED in AI-economics research. For the cumulative state of evidence on any topic, see the /syntheses pages. A single study rarely overturns a body of evidence.

Strengthened: Evidence that ethics-governance and institutional accountability, not model capability alone, shape adoption and perceived legitimacy gained weight this week.

Newly observed: A randomized experiment finds that disclosing AI as the decision-maker in corporate social responsibility (CSR) lowers perceived competence and authenticity, and a separate survey links over-explaining generative AI (GenAI) to reduced delegation via cognitive overload.

Better measured: Simulations indicate AI adoption can lower true market beta but typical samples lack power to detect it, helping explain why many asset-pricing tests may return nulls.

Newsuggestive

The antecedents, consequences and repair mechanisms of public distrust: A systematic literature review

Agni Shanti Mayangsari, Badri Munir Sukoco, Reinhard Bachmann, Juansih, Elisabeth Supriharyanti

systematic review, suggestive

Synthesizing 54 studies across sectors and geographies, the review suggests transparency alone is often insufficient to repair distrust and that relational and structural reforms are frequently required alongside disclosure.

So what: If this generalizes: over-reliance on transparency tools risks stalled adoption because the trust gap sits in governance and accountability rather than documentation.

Full numbers

Extendsestablished

AI-Led or Human-Led? Disclosure of the CSR Decision-Maker, Motive Attribution, and Perceived CSR Authenticity

Keonyoung Park, Dongqing Xu, Jiamin Xie

RCT, high evidence

A randomized controlled experiment finds that stating an AI system made the CSR decision reduces perceived competence, fairness, and authenticity relative to human-led disclosure.

So what: If this holds, reputation risk from AI-attribution can offset intended legitimacy benefits, especially where stakeholders read moral intent into corporate actions.

Full numbers

Newdescriptive

Ethnographies of Human‐AI Collaborations: What Arrangements of Expertise Emerge?

Netta Avnon

ethnographic synthesis, descriptive

Across professions and platforms, the synthesis identifies four recurring human-AI arrangements, noting that attempts at full automation frequently fall short, while hybrid co-production and algorithmic control are linked to divergent productivity and power outcomes.

So what: In this sample, realized value depends on role and accountability design, so productivity gains may be uneven even with the same technical system.

Full numbers

Newdescriptive Branching regulatory biographies of GPT-4 and DeepSeek-R1: A BOAP-informed analysis of regulatory reachability in the European Union, the United States and China Yiping Cao Documentary analysis argues open-weight forks can increase competition but may fracture regulatory reachability, while hosted branches may remain more directly governable.

Newsuggestive Organizational AI adoption and financial risk: a Monte Carlo asset pricing framework and portfolio analysis Evanthia K. Zervoudi, Apostolos Christopoulos Simulations indicate firm-level AI adoption can lower true market beta but the effect is often hidden by estimation noise in realistic samples.

Newsuggestive Maintenance Dynamics in Digital Health Public Goods: A Quasi-Experimental Study of Issue Resolution During the 2025 USAID Funding Disruption Jinyou Sheng, Amy Finnegan A donor shock is associated with differential changes in bug resolution, with revenue-driven open-source projects slowing while donor-driven ones stayed stable in this natural experiment.

Newdescriptive Invisible gains: how resistance contests the promised social benefits of generative AI in SMEs Alejandro Ramirez Dutch SME cases report hidden verification work and accountability gaps accompanying GenAI, complicating net efficiency claims.

Extendssuggestive The primacy of ethical governance: Unraveling the AI-HRM adoption paradox in an emerging economy Aunchistha Poo-Udom A Thailand survey links ethical governance more strongly than technical ease to human resources (HR) practitioners’ intent to use AI.

Tensionsuggestive How Does Green and AI ‐Related Innovation Activity Influence Ecological Decoupling in the U.S.? Evidence From Aggregate and Industry‐Adjusted Measures Md. Rashed, Md. Kamal Uddin, Md. Naeemur Rahman, Mohammad Fakhrul Islam, A. K. M. Mohsin, Md. Faisal‐E‐Alam Time-series associations suggest long-run national improvements alongside short-run industry-intensity rebounds when AI and green innovation co-move.

Newdescriptive Should Businesses Trust AI Advice? A Methodology to Audit the Ethical Integrity of Chatbots Manuel Chaves-Maza The Adaptive Ethical Evaluation Protocol (AEEP) audit reports consistent cross-model differences and high correlation with expert ratings, presenting an operational ethics screen for large language model (LLM) advisors.

Extendssuggestive Using large language models for legal decision-making in Austrian value-added tax law: a comparative study Marina Luketina, Andrea Benkel, Christoph G. Schuetz Fine-tuned and retrieval-augmented generation (RAG) LLMs assist routine value-added tax (VAT) analysis yet still hallucinate, indicating limits to full automation in advisory workflows.

Extendssuggestive Dual-pilot policy, supervisory technology and overseas technology investment: Evidence from high-tech firms in China Hanrui Wu, Jingyi Li, Yao Chen, Dewen Liu A quasi-experimental panel links complementary place-based pilots to higher overseas tech investment, moderated in an inverted-U by supervisory procurement.

Tensiondescriptive Decision delegation to GenAI agents in travel planning: Responsible AI signals, delegation levels, and the transparency paradox Sanjit K. Roy, Gaganpreet Singh, S. Mostafa Rasoolimanesh, Ronnie Das, Ali N. Tehrani Survey evidence associates reliability/accountability signals with higher delegation but finds excessive explainability is associated with lower delegation via cognitive overload.

Newsuggestive When diversity cuts both ways: top management team heterogeneity, AI strategic orientation, and firm value creation efficiency Zhidi Yin, Jiamei Che In this panel, associations link top management team (TMT) heterogeneity to lower value-creation efficiency but higher disclosed AI strategic orientation, indicating disclosure may not track implementation.

Newdescriptive Artificial intelligence adoption in accounting and auditing: The technology–organisation–environment (TOE) framework perspective Betül Alkan Interviews indicate managerial support is associated with adoption while data-integration and regulatory uncertainty are reported as constraints, especially outside Big Four contexts.

Newframework Minds and machines: Rethinking absorptive capacity in the age of artificial intelligence Mattia Pedota Proposes AI-specific absorptive capacity dimensions around data/model acquisition and human–AI orchestration.

Governance and trust: A systematic review synthesizes evidence that transparency without relational and structural repair rarely rebuilds trust, and the CSR RCT provides causal evidence that AI-led disclosure can depress perceived authenticity; paired with a survey suggesting transparency overload, the balance tilts further toward governance quality over disclosure volume.

Roles and hidden labor: Ethnographic synthesis and SME cases reinforce that hybrid arrangements create new verification and coordination work that often goes unrecognized, while a workflow pilot suggests load can fall when roles and tooling are redesigned, highlighting heterogeneity that is likely across firms.

Market measurement: Monte Carlo asset-pricing work clarifies that even if AI adoption shifts true betas, finite-sample noise can mask the signal, implying that recent nulls in empirical tests may be power-limited rather than effect-absent.

Transparency helps or hurts acceptance

Full automation versus expert augmentation

Hidden labor versus cognitive-load relief

Regulatory reachability in open-weight ecosystems

Adaptive Ethical Evaluation Protocol (AEEP), Should Businesses Trust AI Advice? A Methodology to Audit the Ethical Integrity of Chatbots: A branched audit that reports strong correlation with expert judgments, enabling repeatable ethics screening of large language model (LLM) advisors.

Monte Carlo CAPM for AI adoption effects, Organizational AI adoption and financial risk: a Monte Carlo asset pricing framework and portfolio analysis: Quantifies mechanical beta shifts from adoption and the finite-sample power limits that obscure them in practice under the capital asset pricing model (CAPM).

Dynamic fsQCA with DEMATEL-AISM, How multiple pressures shape enterprises’ Innovation Paths? A case analysis of China’s new energy vehicle (NEV) sector: Maps equifinal policy-market-resource configurations that yield high innovation and identifies core-periphery influence among pressures using fuzzy set Qualitative Comparative Analysis (fsQCA) with Decision-Making Trial and Evaluation Laboratory (DEMATEL) and a variant of Interpretive Structural Modeling (AISM).

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