The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
Generative AI at Work
Open accessAbstract
We study the staggered introduction of a generative AI-based conversational assistant using data from 5,179 customer support agents.Access to the tool increases productivity, as measured by issues resolved per hour, by 14 percent on average, with the greatest impact on novice and lowskilled workers, and minimal impact on experienced and highly skilled workers.We provide suggestive evidence that the AI model disseminates the potentially tacit knowledge of more able workers and helps newer workers move down the experience curve.In addition, we show that AI assistance improves customer sentiment, reduces requests for managerial intervention, and improves employee retention.
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17 Own result · 6 Own method · 1 No basis given
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Access to the tool increases productivity, as measured by issues resolved per hour, by 14% on average”
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In addition, we find that AI assistance improves customer sentiment, increases employee retention, and may lead to worker learning.”
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In the paper
“We document within-agent changes in communication following AI deployment, with larger changes for lower-skill workers. Acrossperson, we show that these changes increase the similarity of communication patterns between lowand high-skill agents.”
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In the paper
“Using information on software outages in which AI assistance is temporarily unavailable, we provide evidence that exposure to AI leads to durable changes in worker skills. We find that workers exposed to AI recommendations continue to perform better during outages”
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“we show that workers are selective about the recommendations they adopt, following the recommendations 35% on average.”
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In the paper
“In Panel A, we show that the productivity impact of AI assistance is most pronounced for workers in the lowest skill quintile (leftmost side), who see a .29 log point or 34% increase in resolutions per hour.”
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These changes come alongside a substantial decrease in worker attrition, which is driven by the retention of newer workers.”
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Agents with less than 1 month of tenure improve their resolutions per hour by .38 log points or 46% improvement (relative to agents of the same tenure who do not have access to AI assistance). In contrast, we see no effect for agents with more than a year of tenure.”
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“including a 34% improvement for novice and low-skilled workers but with minimal impact on experienced and highly skilled workers.”
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“with agent and tenure fixed effects, we find that the deployment of AI increases RPH by 0.30 chats or 13.8%.”
Claims other papers make about this one
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The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Generative AI models often require significant computational power and resources for training and inference such as GPUs or cloud computing services [8, 15].”
From Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024) · cites this paper as [8]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“However, generative AI technologies like GANs and LLMs are complex to understand and implement [8].”
From Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024) · cites this paper as [8]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“matching prior findings that generative AI assistance reduces the “skill gap” by providing the largest benefits for the weakest students (7–9).”
From Generative AI without guardrails can harm learning: Evidence from high school mathematics (2025) · cites this paper as [8]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“LLM can be used to summarize and synthesize vast amounts of information (eg, legislative analysis tools), manage customer inquiries (eg, chatbot customer service applications), support organizational decision-making (eg, resume screening and job matching platforms) and generate technical and creative content (eg, content ideation tools) (17–21).”
From Large language model exposure and precarious occupations: Unpacking relationships in the Canadian labor force (2026) · cites this paper as [17]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In all three instances, AI tools supplemented expertise rather than displaced experts.”
From Applying AI to Rebuild Middle Class Jobs (2024) · cites this paper as Brynjolfsson et al., 2023
Experimental — accuracy not yet measured
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“AI tools helped novice workers attain the capabilities of experienced agents in three months rather than 10.”
From Applying AI to Rebuild Middle Class Jobs (2024) · cites this paper as Brynjolfsson et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Buffered by the AI tool, support workers experienced significantly less hostility both from their clients and also toward their clients.”
From Applying AI to Rebuild Middle Class Jobs (2024) · cites this paper as Brynjolfsson et al., 2023
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“As an unexpected side-effect, quit rates among new agents also fell substantially, likely due to less customer rage directed at them in chat windows.”
From Applying AI to Rebuild Middle Class Jobs (2024) · cites this paper as Brynjolfsson et al., 2023
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“However, the field of AI is evolving rapidly, particularly in areas such as Generative AI (Brynjolfsson et al. 2023) and Large Language Models (Min et al. 2023).”
From Artificial intelligence for literature reviews: opportunities and challenges (2024) · cites this paper as Brynjolfsson et al. 2023
The cited source says what the claim attributes to it.
In the citing paper
“That generative AI particularly benefited less able writers is paralleled in recent studies focusing on other domains in which generative AI has been shown to help less productive workers (4, 5).”
From Generative artificial intelligence enhances creativity but reduces the diversity of novel content (2023) · cites this paper as [5]
The cited source says what the claim attributes to it.
In the citing paper
“Previous studies have found that LLMs increase productivity the most for low-performing workers (Brynjolfsson et al. 2023, Noy and Zhang 2023, Dell'Acqua et al. 2023).”
From AI Meets the Classroom: When Do Large Language Models Harm Learning? (2024) · cites this paper as Brynjolfsson et al. 2023
The cited source says what the claim attributes to it.
In the citing paper
“Generative AI requires large amounts of data to train models effectively [8].”
From Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024) · cites this paper as [8]
The cited source says what the claim attributes to it.
In the citing paper
“Generative AI models learn from data [8], so the healthcare organisation needs to collect and prepare relevant data for training the models.”
From Generative AI in healthcare: an implementation science informed translational path on application, integration and governance (2024) · cites this paper as [8]
The cited source says what the claim attributes to it.
In the citing paper
“Broadly speaking, these studies have focused on productivity, finding that workers can perform knowledge-intensive tasks significantly more efficiently when given access to generative AI.”
From Generative AI without guardrails can harm learning: Evidence from high school mathematics (2025) · cites this paper as [8]
The cited source says what the claim attributes to it.
In the citing paper
“These results are consistent with the existing literature on the productivity effects of generative AI (7, 8).”
From Generative AI without guardrails can harm learning: Evidence from high school mathematics (2025) · cites this paper as [8]
The cited source says what the claim attributes to it.
In the citing paper
“Unlike in workplaces (Brynjolfsson et al. 2023, Dell'Acqua et al. 2023), LLMs appear to increase inequality in education.”
From AI Meets the Classroom: When Do Large Language Models Harm Learning? (2024) · cites this paper as Brynjolfsson et al. 2023
The cited source says what the claim attributes to it.
In the citing paper
“Many of the reported productivity gains of AI have occurred in cases in which the technology complemented human work (Fügener et al. 2022, Brynjolfsson et al. 2023, Dell'Acqua et al. 2023, Keppler et al. 2024).”
From AI Meets the Classroom: When Do Large Language Models Harm Learning? (2024) · cites this paper as Brynjolfsson et al. 2023
The cited source says what the claim attributes to it.
In the citing paper
“They also estimated a significant, roughly 14% improvement in productivity, and akin to Noy and Zhang's study, these gains were most pronounced among novice workers.”
From Applying AI to Rebuild Middle Class Jobs (2024) · cites this paper as Brynjolfsson et al., 2023
The cited source says what the claim attributes to it.
In the citing paper
“As an unexpected side-effect, quit rates among new agents also fell substantially, likely due to less customer rage directed at them in chat windows.”
From Applying AI to Rebuild Middle Class Jobs (2024) · cites this paper as Brynjolfsson et al., 2023
The cited source says what the claim attributes to it.
In the citing paper
“They also estimated a significant, roughly 14% improvement in productivity, and akin to Noy and Zhang's study, these gains were most pronounced among novice workers.”
From Applying AI to Rebuild Middle Class Jobs (2024) · cites this paper as Brynjolfsson et al., 2023
In the citing paper
“Brynjolfsson et al. (2023) report a 14% productivity increase using Generative AI-based conversational assistants for customer service representatives [9].”
From Sentiment Analysis in the Age of Generative AI (2024) · cites this paper as [9]
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Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ NBER WORKING PAPER SERIES GENERATIVE AI AT WORK > 3.2 Summary Statistics
“Most agents in our sample, 83 percent, are located outside the United States, primarily in the Philippines.”
§ NBER WORKING PAPER SERIES GENERATIVE AI AT WORK > 5 Conclusion
“| Share US Agents | .11 | .15 | .081 | .072 |”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ NBER WORKING PAPER SERIES GENERATIVE AI AT WORK > 4.6.3 Vertical and Horizontal Workflow
“In Column 1 of Table 5, we find that AI assistance generates an almost 25 percent decline in customer requests to speak to a manager.”
§ NBER WORKING PAPER SERIES GENERATIVE AI AT WORK > 5 Conclusion
“| | (1) | (2) | (3) | | --- | --- | --- | --- | | VARIABLES | Leaves this Month Request Manager Transfer | | | | Post AI X Ever Treated | -0.0868** | -0.00875*** | 0.00792 |”
Citations
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“These results are consistent with other studies showing productivity-enhancing and equalizing effects of recent AI technologies (8, 15, 16, 18).”
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“Despite numerous recent studies to address the surge of GAI [31], [31], [34], [34], [35], [35], [36], [36], [37], [38], [39], [40], [41], [44], [45], [46], discussing challenges, tasks, and models, there still needs to be more thorough exploration into the theoretical and mathematical foundations…”
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Generative AI in the Wild: Prospects, Challenges, and Strategies
Sun, Y., Jang, E., Ma, F., & Wang, T. (2024) · Proceedings of the CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642160 · Cited by 64
Lubbe, A., Marais, E., & Kruger, D. J. D. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13476-x · Cited by 90
Communications of the Association for Information SystemsPreprint
Marabelli, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5250701 · Cited by 60
Fouad, A. M., Salem, I. E., & Fathy, E. A. (2024) · Tourism and Hospitality Research · doi:10.1177/14673584241293125 · Cited by 85
“According to Brynjolfsson et al. (2023) and Jo (2023), GAI is founded on the creation of new experiences and information.”
Towards a Definition of Generative Artificial Intelligence
Ronge, R., Maier, M., & Rathgeber, B. (2025) · Philosophy & Technology · doi:10.1007/s13347-025-00863-y · Cited by 87
“These three aspects, combined with the new capabilities of GenAI, may enable productivity gains (with varying intensity) in interaction with GenAI for a variety of sectors (Brynjolfsson et al., 2023) and almost any field of work “from banking to life sciences” (Chui et al., 2023).”
Systematic literature review on bias mitigation in generative AI
Afreen, J., Mohaghegh, M., & Doborjeh, M. (2025) · AI and Ethics · doi:10.1007/s43681-025-00721-9 · Cited by 75
Al-kfairy, M. (2025) · IEEE Engineering Management Review · doi:10.1109/emr.2025.3534034 · Cited by 61
“Support structures such as training sessions and technical support are crucial for helping employees adapt to new tools,increasing organizational buy-in and facilitating the effective leverage of AI’s capabilities [44].”
Generative artificial intelligence in graduate medical education
Janumpally, R., Nanua, S., Ngo, A., & Youens, K. E. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2024.1525604 · Cited by 69
Bienefeld, N., Keller, E., & Grote, G. (2024) · Journal of Medical Internet Research · doi:10.2196/50852 · Cited by 76
Aldasoro, I., Armantier, O., Doerr, S., Gambacorta, L., & Oliviero, T. (2024) · Economics Letters · doi:10.1016/j.econlet.2024.111814 · Cited by 67
Public perception of generative AI on Twitter: an empirical study based on occupation and usage
Miyazaki, K., Murayama, T., Uchiba, T., An, J., & Kwak, H. (2024) · EPJ Data Science · doi:10.1140/epjds/s13688-023-00445-y · Cited by 106
Copiloting the future: How generative AI transforms Software Engineering
Banh, L., Holldack, F., & Strobel, G. (2025) · Information and Software Technology · doi:10.1016/j.infsof.2025.107751 · Cited by 78
Empowering generative AI through mobile edge computing
Ale, L., Zhang, N., King, S. A., & Chen, D. (2024) · Nature Reviews Electrical Engineering · doi:10.1038/s44287-024-00053-6 · Cited by 59
“…marketing, it could simplify dynamic customer engagement 67 ; in medicine, it could facilitate advanced diagnostic tools 6,68 ; in education, it could provide personalized learning experiences 69 ; and in daily work assistance, it could enhance productivity through intelligent task management 70 .”
Ulfsnes, R., Moe, N. B., Stray, V., & Skarpen, M. (2024) · Generative AI for Effective Software Development · doi:10.1007/978-3-031-55642-5_10 · Cited by 62
Kaplan, T. T., & Cankar, M. (2024) · Dental Traumatology · doi:10.1111/edt.12999 · Cited by 55
Global water scarcity footprint by mineral
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/62d19e32-en · Cited by 49
Breakdown of working population by type of occupation in Southern Africa, 2021
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/da1c9bf1-en · Cited by 49
Vulnerable employment, labour productivity and education spending in West Africa, 2000-22
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/8451ad35-en · Cited by 49
Policies for productive and inclusive skills development in Africa
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/0abbeb17-en · Cited by 50
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/603e54b5-en · Cited by 50
Vulnerable employment, labour productivity and education spending in Southern Africa, 2000-22
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/d0e983cf-en · Cited by 49
Employment by education level in mining and mining-related manufacturing in South Africa
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/4d4c0e28-en · Cited by 49
Breakdown of working population by type of occupation in North Africa, 2021
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/9e545b33-en · Cited by 49
Skills for renewable energies in North Africa
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/184b6873-en · Cited by 49
Vulnerable employment, labour productivity and education spending in East Africa, 2000-22
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/662cc03d-en · Cited by 49
Skills for mining in Southern Africa
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/2c2bb158-en · Cited by 49
Export of services in information and communications technology by selected East African countries
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/c7de3aaf-en · Cited by 49
Migrants by level of education, origin and destination (East Africa, 2020)
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/69884fbf-en · Cited by 48
Average years of schooling and learning-adjusted years of schooling in Southern Africa, 2020
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/c7fff4c9-en · Cited by 48
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/e1f48c16-en · Cited by 48
Migrants by level of education, origin and destination (Central Africa, 2020)
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/b8f6d4a6-en · Cited by 48
Average years of schooling and learning-adjusted years of schooling in East Africa, 2020
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/c7d8f102-en · Cited by 48
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/1245413f-en · Cited by 48
Breakdown of working population by type of occupation in West Africa, 2021
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/52c5da51-en · Cited by 48
African Union Commission, & OECD (2024) · Africa’s development dynamics · doi:10.1787/b1826e51-en · Cited by 48
Goh, A. Y. H., Hartanto, A., & Majeed, N. M. (2025) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2025.100845 · Cited by 108
Trust in AI and Top Management Support in Generative-AI Adoption
Korzyński, P., Silva, S. C. L. D. C. E., Górska, A. M., & Mazurek, G. (2024) · Journal of Computer Information Systems · doi:10.1080/08874417.2024.2401986 · Cited by 65
“For example, Brynjolfsson, Li and Raymond 5 found that the utilization of generative AI technology leads to a notable 14% enhancement in productivity levels.”
Babšek, M., Ravšelj, D., Umek, L., & Aristovnik, A. (2025) · AI · doi:10.3390/ai6030044 · Cited by 89
“Another sub-technology of AI is generative AI, which can generate content such as text, images, music, or videos by analyzing patterns in existing data [40].”
Trusting in Generative AI: Catalyst for Employee Performance and Engagement in the Workplace
Marimón, F., Mas‐Machuca, M., & Akhmedova, A. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2388482 · Cited by 79
“Brynjolfsson et al. (2023) come to the same conclusions about the increase of productivity especially of the lower-skilled workers and suggests improvements in worker learning and increases in employee retention levels that occur thanks to the use of GenAI.”
InfluentialKuang, E., Li, M., Fan, M., & Shinohara, K. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642168 · Cited by 70
Ma, Y., Zheng, S., Yang, Z., Pan, H., & Hong, J. (2025) · International Journal of Production Research · doi:10.1080/00207543.2025.2472298 · Cited by 65
Nyberg, A. J., Schleicher, D. J., Bell, B. S., Boon, C., Cappelli, P., Collings, D. G., Dalle Molle, J. E., Feuerriegel, S., Gerhart, B., Jeong, Y., et al. (2025) · Journal of Management · doi:10.1177/01492063251325188 · Cited by 47
Sidra, S., & Mason, C. (2025) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2025.2543997 · Cited by 80
“Consequently, we developed five self-report items to assess the range of benefits that have been documented for collaborative AI tools (Brynjolfsson et al., 2023; Choi & Schwarcz, 2025; Dell ’ Acqua et al., 2023; Noy & Zhang, 2023; Zhang et al., 2022).”
The impact of AI on education and careers: What do students think?
Thomson, S. R., Pickard-Jones, B., Baines, S., & Otermans, P. C. J. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1457299 · Cited by 64
Generative AI in Marketing: Promises, Perils, and Public Policy Implications
Kumar, V., Kotler, P., Gupta, S., & Rajan, B. (2024) · Journal of Public Policy & Marketing · doi:10.1177/07439156241286499 · Cited by 77
“These articles have explored GAI ’ s use in understanding labor productivity (Brynjolfsson, Li, and Raymond 2023), uncovering the prospects and challenges in multiple disciplines (Ooi et al. 2023), managing content marketing (Wahid, Mero, and Ritala 2023), improving marketing functions (Thakur and…”
Jetha, A., Bakhtari, H., Rosella, L. C., Gignac, M. A. M., Biswas, A., Shahidi, F. V., Smith, B. T., Smith, M. J., Mustard, C., Khan, N., et al. (2023) · American Journal of Industrial Medicine · doi:10.1002/ajim.23517 · Cited by 42
The Crowdless Future? Generative AI and Creative Problem Solving
Boussioux, L., N. Lane, J., Zhang, M., Jacimovic, V., & Lakhani, K. R. (2023) · doi:10.2139/ssrn.4533642 · Cited by 343
Winners and losers of generative AI: Early Evidence of Shifts in Freelancer Demand
Teutloff, O., Einsiedler, J., Kässi, O., Braesemann, F., Mishkin, P., & del Rio-Chanona, R. M. (2025) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2024.106845 · Cited by 39
The use of LLMs to annotate data in management research: Foundational guidelines and warnings
Carlson, N. A., & Burbano, V. (2025) · Strategic Management Journal · doi:10.1002/smj.70023 · Cited by 62
Liu, X., & Li, Y. (2025) · Behavioral Sciences · doi:10.3390/bs15020206 · Cited by 58
“Brynjolfsson et al. (2023) also found that adoption of suggestions provided by generative AI significantly contributes to continuous skill enhancement among workers.”
Generative AI Meets Open-Ended Survey Responses: Research Participant Use of AI and Homogenization
Zhang, S., Xu, J., & Alvero, A. (2025) · Sociological Methods & Research · doi:10.1177/00491241251327130 · Cited by 63
The Paradigm Shifts in Artificial Intelligence
Dhar, V. (2024) · Communications of the ACM · doi:10.1145/3664804 · Cited by 49
Generative Artificial Intelligence Literacy: Scale Development and Its Effect on Job Performance
Xin, L., Zhang, L., & Wei, X. (2025) · Behavioral Sciences · doi:10.3390/bs15060811 · Cited by 71
Humanizing GenAI at work: bridging the gap between technological innovation and employee engagement
Manresa, A., Sammour, A., Mas‐Machuca, M., Chen, W., & Botchie, D. (2024) · Journal of Managerial Psychology · doi:10.1108/jmp-05-2024-0356 · Cited by 49
AI in Dental Radiology—Improving the Efficiency of Reporting With ChatGPT: Comparative Study
Stephan, D., Bertsch, A., Burwinkel, M., Vinayahalingam, S., Al‐Nawas, B., Kämmerer, P. W., & Thiem, D. G. E. (2024) · Journal of Medical Internet Research · doi:10.2196/60684 · Cited by 39
Exploring the Impact of ChatGPT on Business School Education: Prospects, Boundaries, and Paradoxes
Vâlcea, S., Hamdani, M. R., & Wang, S. (2024) · Organizational Behavior Teaching Review · doi:10.1177/10525629241261313 · Cited by 50
“LLMs generate coherent, grammatically accurate sentences by learning to predict words based on the statistical representation of the co-occurrence patterns in human-produced text (Brynjolfsson et al., 2023).”
Mobilising new frontiers in digital transformation research: A problematization review
Ashrafi, A., Constantinides, P., Mehandjiev, N., & Thatcher, J. B. (2025) · Information Systems Journal · doi:10.1111/isj.12531 · Cited by 33
A Critical Analysis of Generative AI: Challenges, Opportunities, and Future Research Directions
Uddin, M., Laghari, S. U. A., Alanazi, F., Hussain, S., Mazhar, T., & Rahman, M. A. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10355-z · Cited by 55
Canagasuriam, D., & Lukacik, E. (2024) · International Journal of Selection and Assessment · doi:10.1111/ijsa.12491 · Cited by 42
Metadata
- Authors
- Brynjolfsson, E., Li, D., & Raymond, L.
- Affiliations
- National Bureau of Economic Research (US)
- Venue
- National Bureau of Economic Research
- Publisher
- National Bureau of Economic Research
- Published
- 2023-04
- OpenAlex
- W4366817968
- Cited by
- 995
- References
- 61
- Citations by year
- 2026: 287 · 2025: 301 · 2024: 352 · 2023: 51
- Type
- report
- Language
- en
- Open access
- green
- doi.org
- Locations
- National Bureau of Economic Research (publishedVersion)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Labor market dynamics and wage inequality
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Keywords
- generative AI, chatbots, productivity, employee experience, tacit knowledge, knowledge transfer, employee retention, customer sentiment, management interventions, artificial intelligence adoption
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