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Direction, evidence grade, and study type are AI-generated labels (gpt-5-mini), not human-verified. Syntheses are LLM-written. "Tensions" are machine-detected candidates, not confirmed contradictions. A research-acceleration tool, not peer review. How this is built →

OpenAlex observations cover 82.5% of papers in this view

This is an observation-coverage view of The Commonplace’s indexed corpus, not a global ranking. 3348 of 4057 papers have a latest OpenAlex count under the selected filter; 709 do not.

Observed cumulative citations

Papers are ordered by their latest recorded OpenAlex count. Ties use observation date, then title and paper ID for deterministic ordering.

OpenAlex cumulative citation observations for papers in the selected corpus view, ordered by count. Each row shows the provider and observation date; counts from other providers are not added.
Rank in this view Paper Published OpenAlex cumulative citations Observation
1 Scientists who adopt large language models publish many more preprints — gains of roughly 24–89% depending on field — but much of the extra output is stylistically polished yet substantively weaker. LLM users also draw on a wider, younger literature, forcing journals and funders to rethink how scientific contribution is evaluated. Keigo Kusumegi, Xinyu Yang, Paul Ginsparg, Mathijs de Vaan, Toby Stuart, Yian Yin 72 OpenAlex

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2 AI is not a single force: different kinds of systems—predictive, generative, agentic and embodied—alter expertise, authority and coordination in distinct ways; management theory must specify AI types to understand organizational impact. Dominic Chalmers, Richard ‘Rick’ Hunt, Stella Pachidi, Kristina Potočnik, David Townsend 33 OpenAlex

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3 Large language model agents can stabilize supply chains in simulation: LLM-based consensus and negotiation frameworks cut demand amplification (the bullwhip effect) and outperform baseline restocking and centralized policies in an inventory-management case study, though findings rest on synthetic experiments and specific model/tool choices. Valeria Jannelli, Stefan Schöpf, Matthias Bickel, Torbjørn Netland, Alexandra Brintrup 28 OpenAlex

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4 AI systems reliably perform narrow clinical tasks and speed routine workflows, but physicians remain indispensable: near-term automation will reallocate tasks rather than replace clinicians, with regulatory, robustness, and liability hurdles slowing widespread substitution. R. Obuchowicz, Adam Piórkowski, Karolina Nurzyńska, B. Obuchowicz, Michał Strzelecki, M. Bielecka 2026 24 OpenAlex

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5 Firms that build AI-enabled dynamic capabilities report better performance largely because AI fosters a data-driven mindset that institutionalizes learning; however, too much reliance on data weakens managers' flexibility and reduces marginal gains. Hassan Samih Ayoub, Joshua Chibuike Sopuru 18 OpenAlex

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6 Saudi banks that deploy AI-powered FinTech tools show stronger returns, higher valuations and greater stability, alongside improved ESG metrics; results hold across panel and dynamic models but stem from a small, single-country sample. Amina Hamdouni 18 OpenAlex

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7 In emerging Asia, firms with stronger AI capabilities produce greener innovations more efficiently, but the environmental payoff is substantially larger when firms have robust ESG practices; AI alone is insufficient without governance that fosters transparency and accountability. Marwan Mansour, Mo’taz Al Zobi, Mohammed Alomair 17 OpenAlex

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8 A blueprint for human–AI complementarity: firms that invest in team composition, shared mental models, attention/orchestration, and continuous training can achieve team performance that exceeds humans or AI alone; without these sociotechnical investments, AI’s productivity gains will be limited and uneven. Cleotilde Gonzalez, Kate Donahue, Daniel G Goldstein, Hoda Heidari, Mohammad S. Jalali, Beau G. Schelble, Aarti Singh, Anita Woolley 2026 16 OpenAlex

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9 Hype about AI widens corporate technology gaps at first but can trigger catch-up: modest 'AI washing' suppresses innovation, yet beyond a tipping point firms ramp R&D and narrow the gap; participatory learning eases the harm while high investor sentiment deepens it. Zhe Sun, Yujun Wen, Liang Zhao, Intesar Almugren, Aradhana Galgotia 16 OpenAlex

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10 Tax officials say AI tools improve detection and compliance but practical barriers limit impact: a Palestinian revenue department survey finds predictive analytics and automated auditing are seen to boost accuracy, yet concerns over data privacy, high costs and staff unfamiliarity threaten widescale adoption. Abdallah Salah Hasan Alseikh, Murad Ali Ahmad Al-Zaqeba, Izlawanie Muhammad 16 OpenAlex

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11 A comprehensive survey finds that the promise of large foundation models for human-AI collaboration depends less on model scale and more on human-centered design, preference-driven objective shaping, and governance; the literature is expanding quickly but remains non-systematic and highlights open challenges in bias, evaluation, and socio-economic effects. Vanshika Vats, Marzia Binta Nizam, Minghao Liu, Ziyuan Wang, Richard Ho, Mohnish Sai Prasad, Vincent Titterton, Sai Venkat Malreddy, Riya Aggarwal, Yanwen Xu, Lei Ding, Jay Mehta, Nathan Grinnell, Li Liu, Sijia Zhong, Devanathan Nallur Gandamani, Xinyi Tang, Rohan Ghosalkar, Celeste Shen, Rachel Shen, Nafisa Hussain, Kesav Ravichandran, James Davis 13 OpenAlex

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12 A survey of 627 studies identifies four distinct AI–human decision-making paradigms driven by AI–human dynamics and decision typologies; the framework helps organizations choose between intuitive, algorithmic, analytical and hybrid decision modes as they integrate AI into operations. Han Li, Feng Tian 13 OpenAlex

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13 Algorithmic hiring tools risk hiding and amplifying workplace inequalities: while promised as efficient and objective, recruitment AIs can reproduce bias through opaque models and institutional legitimation, requiring interdisciplinary scrutiny and shared regulatory responsibility. Karen D Hughes, Alla Konnikov, Nicole Denier, Yang Hu 13 OpenAlex

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14 Generative AI adoption is linked to more opportunistic ESG behavior: firms using generative models show stronger environmental scores but weaker social and governance performance, a pattern amplified across supply chains and by strict regulation and green investor pressure but mitigated by analyst scrutiny and better disclosures. Zhe Sun, Lei Liu, Liang Zhao, Hind Alofaysan, Bhumika Gupta 11 OpenAlex

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15 Large language models match human persuaders on average, but results swing widely by context; a meta-analysis of seven studies finds no average advantage for humans or LLMs, while model choice, message design and domain jointly explain most of the variation. Lukas Hölbling, Sebastian Maier, Stefan Feuerriegel 11 OpenAlex

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16 AI can raise radiology accuracy and throughput, but benefits are conditional on real-world integration—poorly designed deployments risk automation bias, deskilling and workflow disruption that can erode clinical and economic gains. B. Koçak, Renato Cuocolo 2026 10 OpenAlex

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17 AI sharpens the lens on ESG reporting—transformer-based NLP scales comparability and flags potential greenwashing—but evidence that managerial deployment of AI actually boosts stakeholder trust or market outcomes is scarce, and language and explainability shortfalls constrain practical use. Jiacheng Liu, Ye Yuan, Zhelun Zhu 10 OpenAlex

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18 Bench tests show SAP IBP excels under stable conditions but is slow to adapt when demand turns volatile; custom AI planning engines reconfigure faster, reduce planner effort and facilitate quicker scenario exploration. Divya Soundarapandian 10 OpenAlex

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19 Clear, high-quality data builds trust in AI decision-support systems, and that trust helps drive organizations' intentions to adopt them; those intentions correlate with higher self-reported decision-making efficiency. G. Bondac, S. Stanescu, C. Ionescu, Anisoara Duica, Marilena Carmen Uzlău 2026 10 OpenAlex

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20 Countries with stronger AI readiness record notably better progress on the Sustainable Development Goals, and FinTech and Blockchain activity also predict higher SDG performance; when nations develop multiple digital technologies together, the combined sustainability gains exceed the sum of individual effects. Nesrine Gafsi, Amina Hamdouni, Aida Smaoui 10 OpenAlex

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21 Passive, copy-based use of AI erodes workers' confidence, ownership and sense of meaningful work — and some harms persist after returning to manual tasks; by contrast, active, draft-first collaboration with AI preserves worker agency and avoids these psychological costs. Elena Hayoung Lee, Yidan Yin, Nan Jia, Cheryl Wakslak 10 OpenAlex

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22 Tailored explainable-AI boosts gig workers' acceptance, but too much explanation backfires: local or counterfactual reasons raise trust and improve manager-worker relations, while combining both overwhelms workers and reduces acceptance. Miles M. Yang, Ying Lu, Fang Lee Cooke 10 OpenAlex

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23 Academic research on AI credit scoring delivers strong predictive results but treats fairness and explainability as separate issues, leaving scarce guidance for regulated, real‑world use; the literature rarely evaluates integrated approaches that meet oversight and accountability requirements. Rashed Bahlool, Nabil Hewahi, Wael Elmedany 9 OpenAlex

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24 Detailed message-level analysis of 19 verified harm cases finds frequent chatbot misrepresentations of sentience and numerous user expressions of suicidal ideation and delusional thinking, with harmful dynamics amplifying over long multi-turn conversations — a pattern that raises regulatory, liability and product-design concerns for LLM providers. Jared Moore, Ashish Mehta, William Agnew, Jacy Reese Anthis, Ryan Louie, Yifan Mai, Peggy Yin, Myra Cheng, Samuel J Paech, Kevin Klyman, Stevie Chancellor, Eric Lin, Nick Haber, Desmond C. Ong 9 OpenAlex

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25 A practical risk-based playbook for government AI: classify systems by impact and require accountability, human oversight, impact assessments, fairness testing, secure audit trails and enforceable procurement clauses for high-risk uses; a phased roadmap helps resource-constrained administrations deploy beneficial AI while protecting rights and trust. NAVEED RAFAQAT AHMAD 8 OpenAlex

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Showing the first 25 of 3348 observed papers.

Publication-year context

Citation counts generally accumulate over time. Cohort medians use observed OpenAlex counts only, while the observed and missing columns keep the full corpus denominator visible.

Paper coverage and median observed OpenAlex cumulative citations by publication year.
Publication year Corpus papers Observed Missing Median observed citations
2030 1 1 0 0
2026 3734 3055 679 0
2025 317 292 25 0.0
Unknown publication year 5 0 5 No observations

How to read these counts

Counts are cumulative provider observations captured on the displayed dates. Citation practices differ by field and publication age, and provider coverage changes over time. These counts do not establish quality, correctness, causal influence, or societal impact.

Review coverage quality for corpus limitations and the intake pipeline documentation for how The Commonplace collects and assesses research.