Evidence (1286 claims)

The paper proposes an original 'Revenue-Sharing as Infrastructure' (RSI) model in which the platform offers its AI infrastructure for free and takes a percentage of the revenues generated by developers' applications, reversing the traditional upstream payment logic.

Recent literature distinguishes three generations of business models: a first generation modeled on cloud computing (pay-per-use), a second characterized by diversification (freemium, subscriptions), and a third, emerging generation exploring multi-layer market architectures with revenue-sharing mechanisms.

The study collected data from 293 questionnaire respondents and 12 interview participants.

We construct a multidimensional energy justice index to analyze AI’s net effects, pathways, and institutional dependencies.

This study uses a panel dataset for 30 Chinese provinces from 2008 to 2022.

Despite fears of mass unemployment, aggregate labor-market data through 2025 show limited labor-market disruption from generative AI.

The interaction between selection and recourse generates a closed-loop dynamical system linking candidate selection and strategic recourse.

This setting produces endogenous selection, in which both the decision rule and the selection threshold are determined by the population's current feature state.

The success benchmark evolves endogenously as many candidates adjust simultaneously.

The study proposes a framework that models recourse as a strategic interaction among candidates under a risk-based selection rule.

Actionable recourse studies whether individuals can modify feasible features to overturn unfavorable outcomes produced by AI-assisted decision-support systems.

No original quantitative dataset or controlled evaluation is reported in this paper.

The paper is a position/normative paper (not an empirical study) that uses conceptual analysis, literature synthesis, and prescriptive roadmaping rather than new quantitative experiments or datasets.

There is a need for longitudinal and cross‑country empirical research to measure how hybrid work and AI tools affect promotion rates, network centrality, productivity, privacy harms, trust, and long‑term career trajectories.

Practical recommendations for firms and policymakers include investing in training for AI curation/evaluation/coordination, experimenting with decentralised decision rights and governance safeguards, and monitoring competitive dynamics related to model/platform providers.

The paper recommends a research agenda for AI economists: causal microeconometric studies (DiD, IVs, RCTs), structural models with hybrid human–AI agents, measurement work on GenAI use, distributional analysis and policy evaluation.

Bibliometric mapping profiles the intellectual structure and evolution of the field but does not establish causal effects of GenAI on organisational outcomes.

Co‑word and thematic analyses reveal six coherent conceptual clusters that bridge technical AI topics (e.g., LLMs, GANs) with managerial themes (e.g., autonomy, coordination, decision‑making).

Bibliometric and conceptual tools (VOSviewer, Bibliometrix) were used to identify performance trends, co‑word structures, thematic maps, and conceptual evolution in the GenAI–organisation literature.

The study analysed a corpus of 212 Scopus‑indexed publications covering 2018–2025 to map emergent literature on Generative AI and organisational change.

Research agenda: causal studies (panel data, quasi-experiments) are needed to estimate effects of AI exposure on employment outcomes and to evaluate retraining/income-support interventions for pre-retirement populations.

Study limitations: cross-sectional design, self-reported intentions, potential unobserved confounders, and limited generalizability to only three cities (Beijing, Guangzhou, Lanzhou).

The methodology is normative-philosophical argumentation supplemented by interdisciplinary synthesis (phenomenology, deconstruction, OOO, STS/material turn); this is not an empirical causal study and contains no quantitative datasets.

The paper’s empirical grounding consists of illustrative case studies and vignettes from healthcare robotics, autonomous vehicles, and algorithmic governance used to demonstrate distributed agency and responsibility.

Key measurable metrics for future evaluation include contest frequency and outcomes, time-to-help for different groups, user satisfaction, perceived fairness, incidence of automation bias, and usability/access disparities.

The paper does not report empirical data; instead it provides a vignette and a proposed evaluation agenda (user studies, field pilots, A/B tests, logs, surveys).

The pattern provides an outcome-specific, easy-to-use contest channel allowing users to contest particular decisions without renegotiating global rules.

The pattern requires legibility at the contact point so the robot clearly communicates which active mode is in use and why when deferring or prioritizing.

The pattern constrains prioritization to a governance-approved menu of admissible modes, limiting the policy space to vetted options.

Pilot randomized or quasi-experimental implementations of reduced workweeks (across firms, industries, or regions) are needed to measure effects on employment, productivity, wages, and consumption.

There is limited direct causal identification separating technology-driven layoffs from incentive-driven layoffs in current firm-level data, creating a need for new firm-panel datasets linking AI adoption, executive pay/ownership, layoff decisions, and local demand outcomes.

Observed layoffs should be treated in empirical research as outcomes of firm governance and incentive structures; econometric studies estimating displacement from AI must control for managerial incentives and financial pressures.

Research priorities include empirical testing and simulation of ISB-based control systems, cost–benefit analysis of proactive versus reactive AI governance, and distributional impact assessments.

This work is conceptual/theoretical and reports no original empirical dataset; it explicitly calls for mixed-methods empirical validation (case studies, field experiments, longitudinal studies), measurement development, and multi-level data collection.

This research conducts a critical analysis of the ethical implications of artificial intelligence in terms of job displacement during the fifth industrial revolution.

The study contributes to the literature by integrating evidence across higher education, vocational training, and lifelong learning to emphasize the need for balanced policy approaches to skill formation.

The study uses secondary data and comparative evidence from prior empirical studies to analyze relationships between higher education, vocational education, and lifelong learning.

The paper explores risk frameworks, ethical constraints, and policy imperatives related to AI.

This paper investigates societal applications of AI across domains such as healthcare, education, accessibility, environmental management, emergency response, and civic administration.

The paper explains the main legal frameworks that currently regulate AI in India, as well as proposals for future legislation.

A “macro approach” that (1) directly models equilibrium behavior of large employers, (2) combines macro data with empirical estimates of employers’ responses (from the micro approach) to estimate the model, and (3) uses the model to compute aggregate costs of monopsony and optimal policies, is the appropriate methodological response.

The traditional theoretical and empirical “micro approach” to studying labor market power requires that firms are small and atomistic.

Social assistance (SA) is defined here as noncontributory social transfers (including cash, vouchers, or in-kind transfers to families or individuals, including the elderly), public works programs, fee waivers, and subsidies.

This chapter focuses on low- and middle-income countries (LMICs) and uses a 'review of reviews' approach to summarize the policy discourse and evidence on social protection and gender in adulthood, concentrating on social assistance, social care, and social insurance.

This study draws on a critical AI media literacy framework to analyze user-generated discussions in the two largest higher education subreddits on Reddit.com.

The study used a mixed-methods design incorporating surveys from 150 LEP immigrants, interviews with 50 employers, and interviews with 20 translation service providers in various linguistically diverse U.S. cities, with quantitative analysis performed in SPSS Version 28 and qualitative thematic coding in NVivo 14.

We extract the Big 5 personality traits from facial images of 96,000 MBA graduates using advances in AI and LinkedIn microdata.

The essay reviews seven books from the past dozen years by social scientists examining the economic impact of artificial intelligence (AI).

A composite index capturing concerns about mental health, privacy, climate impact, and labor market disruption was constructed to measure societal risk perceptions of AI.

This study uses a conceptual and analytical approach to examine the impact of AI and automation on work.