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OpenAlex observations cover 92.1% of papers in this view

This is an observation-coverage view of The Commonplace’s indexed corpus, not a global ranking. 292 of 317 papers have a latest OpenAlex count under the selected filter; 25 do not.

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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 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 32 OpenAlex

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2 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 20 OpenAlex

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3 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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4 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 14 OpenAlex

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5 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 13 OpenAlex

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6 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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7 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 11 OpenAlex

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8 Enterprise generative and agentic AI tools are linked to measurable productivity and workflow gains across firms, but current evidence offers no consensus on whether these technologies produce net job losses or wage changes; policymakers should treat labor-impact claims as provisional and prioritize targeted reskilling and rigorous field evaluations. George Lazaroiu, Tom Gedeon, Xavier Fernando, Mihaela Herciu, Gheorghe Grecu, Claudiu Chiru, Iulia Grecu, Iuliana Pârvu, Claudia Guni 9 OpenAlex

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9 Machine learning can cut enterprise forecasting errors by roughly 15–40% compared with traditional rule-based systems, but the payoff depends on firm data, talent and governance. Emerging solutions—explainable AI, AutoML and federated learning—promise to reduce implementation and regulatory frictions that currently limit broad adoption. Zifan Chen, Jingyi Liu, Jiaying Chen 8 OpenAlex

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10 A multi-agent LLM tool (PKAI) helps business process analysts produce more accurate and useful conceptual models in controlled tests and demonstrates promise in a real-world deployment; however, evidence rests on a quasi-experiment and a single field case, limiting broad claims about productivity gains. Malik Schinckus, Anthony Simonofski, Nicolás Bono Rosselló 6 OpenAlex

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11 AI growth in Chinese provinces is linked to stronger green innovation resilience, driven chiefly by upgrades in industrial structure's quantity and quality rather than by rationalizing industry mix. Public environmental concern must remain within a moderate range for benefits to hold, while tighter environmental regulation amplifies AI's green-innovation gains in a stepwise fashion. Le Yan, Wei Li, Shizheng Tan, Xiaoguang Liu 5 OpenAlex

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12 AI-driven controls and interoperable APIs boost banks’ digital maturity and customer outcomes, but gains only materialize with strong model-risk governance and careful third‑party oversight; legacy systems, cyber threats and regulatory fragmentation remain binding constraints. Bothaina Alsobai, Dalal Aassouli 5 OpenAlex

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13 Generative AI outputs belong in the public domain: granting copyright to raw algorithmic output would enclose the digital commons and chill innovation, while only discernible human creative contributions should qualify for protection. Ezieddin Elmahjub 5 OpenAlex

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14 Leading multimodal AI systems struggle to detect greenwashing in Polish ESG reports, frequently flagging high-performing firms as more deceptive and showing virtually no agreement across models; current off‑the‑shelf tools conflate polished sustainability communication with fraudulent intent and lack the contextual grounding for reliable ESG assurance. Jacek Krzysztof Jakubczak, Dorota Chmielewska-Muciek, Katarzyna Iwanicka 5 OpenAlex

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15 Time pressure makes people take mental shortcuts and miss AI mistakes, cutting the accuracy of human–AI teams; task complexity and erroneous AI advice further harm performance, with interactions that diverge from classic dual-process expectations. Lukas Hermanns, Timm Teubner 5 OpenAlex

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16 Employees in moderately complex jobs are most ready to adopt generative AI: both low- and high-complexity roles report lower AI confidence and fewer adoption behaviors, challenging the assumption that highly skilled roles naturally lead AI integration. Mustafa Akben, Su Dong 4 OpenAlex

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17 China's AI policy is linked to higher green innovation among listed manufacturers, primarily by encouraging industry clustering and broader knowledge mixes; the boost is strongest for smaller, non‑state, high‑tech and highly competitive firms. Jiahui Liu, Chun Yan 3 OpenAlex

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18 China’s AI pilot zones raise urban land green-use efficiency by spurring green innovation, shifting labor and upgrading industry; gains are largest in big, digitally well-equipped cities and spill over to nearby regions. Shanshan Zhu, Yaping Zhang, Zerun Wang 3 OpenAlex

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19 Chinese cities with more advanced AI development show stronger industrial green transformation driven by green innovation and agglomeration, but gains concentrate locally and appear to slow green progress in neighbouring cities. Weiping Zeng, Zihui Yin 3 OpenAlex

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20 Deep-learning models, led by GRUs, forecast a simulated Thai ESG stock index substantially better than classical ARIMA-style approaches over a 36-day horizon. The advantage endures even with limited historical data, implying advanced AI can strengthen ESG market signals in data-constrained emerging markets. Umawadee Detthamrong, Rapeepat Klangbunrueang, Wirapong Chansanam, Rasita Dasri 3 OpenAlex

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21 Digital tools helped Chinese tourism firms weather COVID: listed firms with more digitalization—notably AI and big data—suffered smaller stock-price losses and secured finance more easily, driven by diversified supply chains and greater investor attention. Xijia Huang, Haibin Wu, Dashan Liu, Xinchen Liu, Lisi Yang 3 OpenAlex

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22 EU countries with stronger AI uptake tend to use materials more productively, but gains on waste reduction and recycling are patchy; AI appears to support circular-economy goals mainly when paired with coherent national strategies and institutions. Anca Antoaneta Vărzaru 3 OpenAlex

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23 Machine learning is reshaping work: a systematic review finds AI powering algorithmic management, platform coordination and safety monitoring, but raises urgent transparency and fairness issues under the EU's 2024 AI rules. Nikita Kalganov, Amir Mosavi, Csaba Mako 3 OpenAlex

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24 Strong data governance, not just smarter algorithms, determines whether AI improves financial decisions; a systematic review of 1,155 studies finds governance maturity mediates the link between AI integration and better financial outcomes, with large estimated effects but based on aggregated observational evidence. Phaktada Choowan, Hanvedes Daovisan 3 OpenAlex

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25 Telling firms how many rivals use advanced machines nudges adoption: an information treatment in a Bank of Italy survey raised firms' intended uptake of robotics but left AI intentions unchanged, highlighting information frictions as a brake on technology diffusion. Zoë Cullen, Ester Faia, Elisa Guglielminetti, Ricardo Perez-Truglia, Concetta Rondinelli 3 OpenAlex

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Showing the first 25 of 292 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
2025 317 292 25 0.0

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.